can be explained by the natural resiliency of some videos to packet loss due
to different video characteristics. Video sequences with low motion intensity
are more resilient to losses, and generally, have better results in the QoE-
aware assessment. On the other hand, videos with high motion intensity tend to
have poor results.

Figure 33: SSIM for all scenarios with two UAVs

As the number of video sequence flows begins to increase, the quality of the
transmitted video starts to decrease sooner than before. Figures 34 and 35
depict this tendency. In the first two scenarios (with one and two UAVs), the
uavFEC managed to keep the SSIM above 0.7 up to 700m (other approaches only up
to 600m). However, with three and four UAVs, the uavFEC was able to maintain
the SSIM over 0.7 only up to 600m, after that, there is a sharp decline in the
video quality in all of the assessed mechanisms. This can be attributed to a
more congested network due to several transmissions together with the distance
from the ground control station.

Figure 34: SSIM for all scenarios with three UAVs Figure 35: SSIM for all
scenarios with four UAVs

#####  24.3.3 Network footprint analysis

Throughout the QoE assessment was demonstrated that the uavFEC mechanism
enhances the video quality over several scenarios, having particularly good
results over higher distances and with increased network traffic. Besides the
video quality, the uavFEC was also designed to add as little as possible
redundancy, to maintain a low overhead and thus saving resources.

The network overhead was computed by summing the size of all video frames
transmitted by each mechanism. This means that, if the original frame size is
subtracted, it is possible to find the specific amount of redundancy added
only by the approaches. Two mechanisms assessed are non-adaptive, video-aware
FEC and ViewFEC, and because of that, they have the same network overhead in
all distances, which was 65.10% and 38.90%, respectively, as showed in Figure
36. These mechanisms are not appropriate because even when the UAVs are close
to the ground control station they add a considerable amount of redundancy,
wasting resources. The same figure depicts the results for uavFEC and CLM-UEP.
Both mechanisms perform close to each other up to 600m, but in average the
uavFEC has lower network overhead. Over 600m, the uavFEC starts to add more
redundancy, increasing the network overhead, however, providing better video
quality.

Figure 36: Network overhead for all scenarios

#####  24.3.4 Overall results

Figure 37 depicts the comparison of uavFEC and the related work (CLM-UEP) [Lin
et al., 2012a]. The graph shows the average percentage of QoE improvement
against the amount of redundancy added by the mechanisms in all scenarios
(from 1 to 4 UAVs). A positive percentage means that uavFEC had better QoE
results than CLM-UEP. In all four scenarios, uavFEC presented a slightly
better video quality until 600m, on average between 0.59% and 5.00% better.
The real advantage of uavFEC is noticeable after the 700m when it enhances
even further the video quality. The uavFEC was able to achieve improvements,
on average, between 11.59% and 28.52%, better than CLM-UEP. Taking this into
consideration, it is clear that the proposed mechanism performs better in a
higher distance, where the PLR is also higher. This gives uavFEC the
capability to operate in wide coverage areas.

Figure 37: QoE and Redundancy against UAV distance

It is also shown in Figure 37 the comparison of the amount of redundancy added
by both, CLM-UEP and uavFEC. A negative percentage means that the proposed
mechanism adds less redundancy than CLM-UEP. In all four scenarios, uavFEC
added less redundancy until 600m, which was around 3.75% and 15.12% less on
average and still managed to transmit the videos with higher QoE. This means
that uavFEC was able to improve the video quality and at the same time save
resources. After 700m, the uavFEC mechanism begins to increase the redundancy.
This happens because the uavFEC was developed to enhance the video quality
over higher distances, which make the networks more susceptible to errors.
Considering this, the mechanism will have to increase the protection of the
most important video data, adding more overhead. For example, at 700m the
uavFEC mechanism added on average 4.49% more redundancy, and at 900m added
10.19%. Increasing the redundancy is an expected response of the proposed
mechanism to further improve the video quality, which can be confirmed through
the QoE assessment in the same figure. In summary, uavFEC provides a good
tradeoff between video quality and network overhead.

A further analysis of Figure 37 shows that up to 500m both mechanisms had
similar QoE results, with uavFEC having a modest higher video quality. The
major difference was the considerably smaller network overhead, this means
that uavFEC, through its QoE- and Video-aware techniques, was able to add
redundancy to the most important video data only. At 600m, the uavFEC
mechanism still adds less redundancy than the related work, but it is already
showing better results, with an improvement of 5.00% on QoE. After this
threshold, considering the increasing distance and in order to improve the
video quality, the proposed mechanism starts to add a larger amount of
redundancy. The result of this approach are videos transmitted on average with
more than 28% of better quality than CLM-UEP while adding less than 11% of
redundancy.

The uavFEC mechanism achieved good results making the video transmission more
resilient to packet loss and thus, enabling a longer video transmission range
for the UAVs. The results are particularly beneficial in a higher distance
with several UAVs, providing a better video quality of live video flows,
allowing end-users, such as civilians and/or authorities, to have a high-
quality perception of videos and thus reducing reaction times.

###  25 Adaptive Motion Intensity Awareness mechanism (MINT-FEC)

The mechanism presented in Section 24 obtained favourable results however, it
presented some drawbacks in regard to the motion intensity classification and
the strict video resolution dependence. To improve on these issues, the
adaptive FEC-based mechanism with motion intensity awareness (MINT-FEC) was
proposed.

####  25.1 MINT-FEC Overview

The MINT-FEC mechanism aims to enhance the resilience of UAV real-time video
transmissions. One of the major weaknesses in the mechanisms found in the
literature, including the uavFEC, is the use of unnecessary redundancy. To
tackle this issue, the MINT-FEC dynamically adapts itself by using fuzzy
logic, to add a precise amount of redundancy to only the most QoE-sensitive
data, while ensuring high-quality video and downsizing the usage of scarce
wireless resources.

In the MINT-FEC mechanism, the motion intensity is now given by combining the
spatial complexity and temporal intensity. Spatial complexity is how distinct
one frame is from another, as well as the colour and luminance saturation. The
temporal intensity can be defined as how fast and how much the image is
changing frame-by-frame. Their joint use provides a more accurate motion
classification to be used for adding a adequate amount of redundancy. Another
improvement from the work described in Section 24 is the video resolution
independence. By normalising the values of all the video characteristics, as
well as using the motion vector distance and macroblock size, it is possible
to add, on-the-fly, an adaptive amount of redundancy to videos with arbitrary
resolution. A detailed description of all the novel components is given next.

Following the same core structure as uavFEC, the MINT-FEC mechanism also
depends on Fuzzy logic and thus several fuzzy components need to be defined,
such as the sets, membership functions, and rules. The offline process needs
to be executed only once. After that, all the generated information can be
loaded into the fuzzy interface engine to be used in real-time.

####  25.2 Towards the design of MINT-FEC

The design process of MINT-FEC starts with the definition of the fuzzy
components. In a similar fashion to the uavFEC mechanism, the first step is to
quantify the spatial complexity. This component represents how much static
information a frame is carrying compared to the previous one. The most common
way to compute this difference is using the Sum of Absolute Differences (SAD)
[Vanne et al., 2006]. It is not a complex operation however, it is very time-
consuming because it compares each pixel from both frames, making this
impractical in real-time.

Another way to locate this information is through the frame sizes. The problem
of using the frame size is that several video characteristics can impact on
it, such as different resolutions (picture size), content, as well as temporal
intensity. To be able to compare the frame sizes among different videos, it is
necessary to normalise all the information. Using Equation (15) the average
frame size is calculated, and the same operation is also executed for P- and
B-frames. After that, through Equation (16), all frame sizes are normalised,
as before, this is also done for P- and B-frames. This process is performed
for each video sequence separately. Table 15 shows the adopted notation.

$\mu I_{s}=\frac{1}{nF}\sum_{i=0}^{nF-1}I_{s(i)}$ (15)

$\hat{\mu}I_{s}=\frac{\mu I_{s}}{\mu I_{s}+\mu P_{s}+\mu B_{s}}$ (16)

Table 15: MINT-FEC Adopted Notation Notation | Meaning
---|---
$\mu I_{s},\mu P_{s},\mu B_{s}$ | Frame size average
$\hat{\mu}I_{s},\hat{\mu}P_{s},\hat{\mu}B_{s}$ | Normalised frame size average
$I_{s(i)},P_{s(i)},B_{s(i)}$ |  Frame size of the $i_{th}$ frame
$nF$ | Number of frames in the video sequence
$\left|MV\right|$ | Euclidean distance of a motion vector
$\left|MV_{(i)}\right|$ |  Euclidean distance of the $i_{th}$ motion vector
$MB_{h}$ | Macroblock height
$MB_{w}$ | Macroblock width
$aMB$ | Macroblock area
$aMB_{(i)}$ |  Area of the $i_{th}$ macroblock
$nMB$ | Number of macroblock in the frame
$TI_{\Delta t}$ | Temporal intensity

Once all the frame sizes are normalised, it is possible to perform an
exploratory analysis to cluster all frames of all video sequences together
according to their sizes. Based on the linkage distance of the clusters it was
possible to divide them into three distinct groups, namely “small”, “medium”,
and “large”. After defining the clusters, a boxplot was used to summarise and
display the distribution of the data. This is an important tool in the
exploratory analysis because it displays the shape of the distribution of each
cluster along with the central value and the variability. Figure 38 shows the
boxplot for the spatial complexity. The fuzzy sets for spatial complexity were
defined using the information displayed by the boxplot, as showed by Algorithm
6.5.

Figure 38: Spatial Complexity

FuzzyOperator& op = FuzzyOperator::DefaultFuzzyOperator();

FuzzyEngine engine(“complex-mamdani”, op);

_InputLVar* Isz = new InputLVar(“I-size”);_

Isz $\rightarrow$ addTerm( ShoulderTerm(“SMALL”, 0.274, 0.459, true));

Isz $\rightarrow$ addTerm( TriangularTerm(“MEDIUM”, 0.274, 0.651));

Isz $\rightarrow$ addTerm( ShoulderTerm(“LARGE”, 0.502, 0.757, true));

engine.addInputLVar(Isz);

_InputLVar* Psz = new InputLVar(“P-size”);_

Psz $\rightarrow$ addTerm( ShoulderTerm(“SMALL”, 0.162, 0.219, true));

Psz $\rightarrow$ addTerm( TriangularTerm(“MEDIUM”, 0.162, 0.325));

Psz $\rightarrow$ addTerm( ShoulderTerm(“LARGE”, 0.288, 0.333, true));

engine.addInputLVar(Psz);

_InputLVar* Bsz = new InputLVar(“B-size”);_

Bsz $\rightarrow$ addTerm( ShoulderTerm(“SMALL”, 0.081, 0.13, true));

Bsz $\rightarrow$ addTerm( TriangularTerm(“MEDIUM”, 0.081, 0.219));

Bsz $\rightarrow$ addTerm( ShoulderTerm(“LARGE”, 0.205, 0.252, true));

engine.addInputLVar(Bsz);

Algorithm 6.5 Spatial complexity (Frame size sets)

After defining the set, the membership functions need to be outlined. This is
problem-dependent, as well as a complex task being difficult to find an
optimal solution [Wong et al., 2005]. Considering that, it is better to use
piecewise linear functions (formed of straight-line sections). These functions
are both simpler and more efficient regarding computability, leading to lesser
resource requirements. Figure 39 shows the graphical representation of the
chosen membership functions for the frame sizes.

Figure 39: Frame size membership function

Apart from the spatial complexity, the fuzzy components for the motion
intensity also need to be created. The analysis of this criterion is performed
through motion vectors details. In order to better infer the amount of
movement described by the vectors, instead of counting them, it is computed
how far each one is pointing using the Euclidean distance. This represents the
motion intensity better because it is possible to have one frame with several
vectors pointing to a close distance meanwhile, another frame with fewer
vectors, pointing farther away although, and thereby having higher motion
intensity.

As defined in the MPEG standard, the motion vectors describe the movement of
macroblocks from some position in one frame to another position in another
frame. It is important to note that not all MB have the same size, as well as
videos with higher resolution will have more macroblocks than videos with
lower resolution. To be able to compare video sequences with different
macroblocks sizes and resolutions, the MB area is used, given by Equation
(17), together with the motion vectors. Additionally, using Equation (18) it
is possible to calculate for each macroblock how many pixels have been moved
and how far away, which can be translated as temporal intensity.

$aMB=MB_{h}\times MB_{w}$ (17) $TI_{\Delta
t}=\frac{1}{nMB}\sum_{i=0}^{nMB-1}aMB_{(i)}\times\left|MV_{(i)}\right|$ (18)

Using the aforementioned details, another exploratory analysis was performed
to classify the video sequences in terms of temporal intensity. This time,
instead of breaking the video sequences in frames, the whole video was
analysed. The values found through Equation (18) were used to cluster the
videos into three distinct groups, namely “low”, “medium”, and “high” temporal
intensity. Additionally, in the same way as before, a boxplot was used to
summarise and display the data distribution, as well as to create the sets, as
presented in Figure 40.

Figure 40: Temporal Intensity

Another important step is to define the PLR. The primary objective is to find
out the influence of different PLR in the QoE for a set of videos. The same
procedure adopted by uavFEC was applied to MINT-FEC however, different values
were found. To find the PLR that best represents the video quality, a number
of network simulations using a broad collection of UAV video sequences were
carried out. As the MINT-FEC can handle arbitrary video resolution, several of
them were used in the experiments. For PLR between 0% and 10%, the video
quality was good, in comparison to the uavFEC, where the good range was
between 0% and 15%. A tolerable video quality was observed for most of the
videos between 5% and 20% of PLR (uavFEC had values between 5% and 30%). Over
15% of PLR the quality quickly decreased in videos with higher motion
intensity, and over 25% it became unacceptable. In general, the PLR input set
is lower in MINF-FEC than in uavFEC because videos with higher resolution tend
to consume more network resources. Based on the results, the PLR set was
defined, as shown in Algorithm 6.6.

_OutputLVar* PLR = new OutputLVar(“PacketLossRate”);_

PLR $\rightarrow$ addTerm( TriangularTerm(“LOW”, 0, 10));

PLR $\rightarrow$ addTerm( TriangularTerm(“MEDIUM”, 5, 20));

PLR $\rightarrow$ addTerm( TriangularTerm(“HIGH”, 15, 100));

engine.addOutputLVar(PLR);

Algorithm 6.6 Packet loss rate input set for arbitrary video resolutions

The last fuzzy set is the redundancy set. The MINT-FEC mechanism uses the same
set as uavFEC, which was explained in Section 24.2 and Algorithm 6.3 defines
it. With all sets delineated, it is necessary to create the rules. This
activity also involves human knowledge about the video characteristics, namely
spatial complexity and temporal intensity, as well as the frame type, and the
PLR. As mentioned before, in videos with high spatial complexity the I-Frame
needs a greater amount of protection, because it holds a large amount of
information. On the other hand, in videos with high temporal intensity, the
I-Frame also needs to be protected. The P-Frame also plays an important role
because it holds the temporal information about that sequence, and needs to
have almost the same protection as the I-Frame. Algorithm 6.7 shows two rules
that represent this case.

RuleBlock* block = new RuleBlock(); _block $\rightarrow$ addRule( new
MamdaniRule(“_

_ _ _if (SpatialComplexity is HIGH and_

__ PacketLossRate is HIGH and

__ FrameType is I)

 __ then

RedundancyAmount is HIGH;

”, engine));

_block $\rightarrow$ addRule( new MamdaniRule(“_

_ _ _if (TemporalIntensity is HIGH and _

__ PacketLossRate is HIGH and

__ FrameType is I or P)

 __ then

RedundancyAmount is HIGH;

”, engine));

Algorithm 6.7 Packet loss x video characteristics rules

MINT-FEC utilises the same core structure of uavFEC, so once all the fuzzy
rules and sets are defined, they are employed in real-time in the fuzzy logic
controller. In the same way as before, the offline process needs to be
performed just once, after that the controller will be able to compute a
suitable amount of QoE-aware redundancy on-the-fly.

####  25.3 MINT-FEC Performance Evaluation and Results

The MINT-FEC goal is to improve on uavFEC (Section 24) to ensure an even
higher perceived QoE for end-users, while avoiding unnecessary network
overhead.

#####  25.3.1 Experiment settings

The assessment scenario consists of up to four UAVs operating in autonomous
mode, with 4G LTE radio at 800MHz. To better reflect a UAV scenario, the
Gauss-Markov distribution mobility model was used. This model provides a
uniform spatial distribution of the nodes and also simulates inertia in the
movements, which are a characteristic of UAVs in autonomous mode. The ground
control station is equipped with a portable base station and antenna. All UAVs
are in line-of-sight and communicating in ad-hoc mode. Only real UAV video
sequences were used in the experiments. To be more precise, twenty of each
video resolution (1080p, 720p, and SVGA), giving a total of sixty video
sequences. All of them were encoded with both same GoP length of 19:2 and same
H.264 codec. Considering the portable base station power, the ad-hoc
communication, and the very demanding high definition videos, the flying range
was limited to a radius of 2000 meters from the base station. Due to the harsh
environment and the low-gain antenna, the PLR can range from 0% to 35%. Figure
41 shows the packet loss distribution in the experiments. At the receiver
side, a Frame-Copy error concealment was used. Table 16 shows the simulation
parameters.

Figure 41: MINT-FEC’s experiment PLR distribution Table 16: MINT-FEC Simulation parameters Parameters | Value
---|---
Display sizes | 1920x1080, 1280x720, and 800x600
Frame rate mode | Constant
Frame rate | 29.970 fps
GoP | 19:2
Video format | H.264
Codec | x264
Container | MP4
Propagation model | FriisPropagationLossModel
Mobility model | Gauss-Markov
UAV velocity | 45-65 km/h (28-40 mph)
LTE Frequency band | 800MHz
LTE Mode | FDD
LTE Bandwidth | 5 MHz
eNodeB Operating Power | 22 dBm
Antenna Gain | 16 dBi

Five different schemes were simulated as follows: (1) without any FEC
mechanism. This is only to serve as a baseline for comparison with the others;
(2) a non-adaptive video-aware FEC (I- and P-Frames are equally protected)
using a pre-set value of 75% of redundancy (Video-aware FEC). This value was
chosen because it showed a good tradeoff between QoE and network overhead in
several PLR; (3) the adaptive FEC-based mechanism (uavFEC), presented in
Section 24; (4) a related work implementation of the Cross-Layer Mapping
Unequal Error Protection (CLM-UEP) [Lin et al., 2012a]. At last, (5) adopts
the proposed MINT-FEC mechanism.

#####  25.3.2 QoE assessments

Figure 42 shows the average of the SSIM results. A foreseen situation can be
clearly noticed, the farther away the UAVs are from the ground control station
the worst is the video quality. In the first case (without FEC), a good video
quality is noticed up to 600m. This is expected on the grounds that some video
sequences tend to have a natural resiliency to packet loss. In particular,
this situation is true in videos with low motion intensity, which usually
scores higher results in QoE assessment. Between 600m and 900m the video
quality is already affected and a sharp decline is perceived after that. At
the same time, in the FEC-based schemes, such as Video-aware FEC, uavFEC, CLM-
UEP and, MINT-FEC, the video quality was good for a long distance, until
1200m. Notwithstanding, the proposed mechanism outperforms all its competitors
in terms of video quality, providing even better results over higher
distances. A comprehensive comparison analysis is given further.

Figure 42: Average SSIM QoE for all scenarios

#####  25.3.3 Network footprint analysis

MINT-FEC can provide enhanced video quality, especially over higher distances,
however, it is equally important to do so with lower network overhead. The
Video-aware FEC scheme is non-adaptive and due to that, it has a constant
network overhead, as showed in Figure 43. This is not suitable for UAVs
because even when they are close to the base station, with a low PLR, a large
amount of redundancy is added, wasting resources. On the other hand, the
adaptive mechanisms (uavFEC, CLM-UEP, and MINT-FEC) allow a better use of the
network resources, as also shown in Figure 43.

Figure 43: Average Network overhead

In all three mechanisms, the initial amount of redundancy is small and starts
to become larger as the UAVs move away from the ground station. Both the
uavFEC mechanism and CLM-UEP perform close to each other up to 1200m. After
that, uavFEC starts to add more redundancy to provide better video quality.
Here again, the MINT-FEC performs better than the others. Up to 1500m, it
induces less network overhead, while providing higher video quality.
Subsequently, it adds a slightly higher redundancy and still smaller than the
other schemes, in favour of a considerably better video quality. This proves
that it was possible to identify the most important video portions and protect
them accordingly.

#####  25.3.4 Overall results

To further understand the MINT-FEC achievements, a comparison against CLM-UEP
and uavFEC is given in Figure 44. The first case is the comparison between
CLM-UEP and MINT-FEC, and the second one, uavFEC against MINT-FEC. The graph
shows the average percentage of QoE and network overhead improvement. In the
QoE assessment, a positive percentage means that the proposed mechanism
achieved higher video quality, which is desirable. On the other hand, in the
network evaluation, a negative percentage means that the MINT-FEC generated
less overhead, which is also advantageous.

Figure 44: QoE and Redundancy comparison

In both cases, MINT-FEC presented a slightly better video quality until 1200m,
which was between 0.59% and 2.01%, and between 0.69% and 1.63%, respectively.
While outperforming the other mechanisms in terms of quality, the proposed
mechanism was also able to considerably reduce the network overhead. It added
on average 16.20% less redundancy than CLM-UEP and 16.65% less redundancy than
uavFEC, up to 1200m. This proves that MINT-FEC is capable of better
identifying the most QoE-sensitive data and adds a precise amount of QoE-aware
redundancy to it, resulting in higher video quality and less network overhead.
After this threshold, the MINT-FEC starts to increase the amount of redundancy
to improve the video quality. This happens for the reason that the proposed
mechanism was designed to sustain a higher video quality over long distances
when the connection is more susceptible to errors. In doing that, the videos
are received with up to 40% better quality in comparison to the CLM-UEP
mechanism and up to 18% higher quality than the uavFEC mechanism. Another
important advance of the MINT-FEC over the uavFEC was the network overhead
reduction beyond 1200m. In this case, the MINT-FEC mechanism managed to reduce
the overhead by up to 11.74%. This is an additional proof that the proposed
mechanism is doing a better work to infer the motion intensity using spatial
complexity and temporal intensity, allowing the definition of a precise amount
of redundant information to the most sensitive data. Thanks to that, it was
possible to deliver higher video quality leading to a better user perception.

###  26 Summary

This chapter investigated the performance of the two proposed mechanisms to
improve the resiliency to packet loss over FANETs using UAV-to-Ground model,
namely uavFEC and MINT-FEC. The results shown in Section 24.3 demonstrated
that the uavFEC mechanism improves video transmissions over highly dynamic
networks, maximising the QoE without adding unnecessary network overhead. The
uavFEC mechanisms was able to provide videos in the best-case scenario with,
on average, between 11.59% and 28.52% better QoE than the main competitor
(CLM-UEP). Overall, the uavFEC mechanism attained good results. It had,
nevertheless, some drawbacks especially due to its strict video resolution
dependence and basic motion intensity classification.

To improve on these issues, the MINT-FEC mechanism was proposed. The achieved
results, shown in Section 25.3, attested that the proposed mechanism
outperforms the other mechanisms in the experiments. In scenarios with up to
1200m, it was able to deliver videos with slightly higher quality and at the
same time generating substantially less network overhead. Therefore, an
enhanced video quality was perceived without wasting wireless resources. On
the other hand, over 1200m, due to harsher conditions, the MINT-FEC mechanism
starts to increase the redundancy providing a considerably higher QoE than the
other mechanisms. This is a desired tradeoff between network overhead and
video quality. The results support the claim that MINT-FEC was able to
identify the motion intensity video sequences as well as to handle arbitrary
video resolutions. At the end, MINT-FEC was able to better protect the most
QoE-sensitive data, which is translated in higher video quality.

The studies and proposed mechanisms in this chapter resulted in the following
publications:

Book chapter:

  * •

Immich, R. and Cerqueira, E. and Curado, M., “Improving video QoE in Unmanned
Aerial Vehicles using an adaptive FEC mechanism”, in Wireless Networking for
Moving Objects: Models, Approaches, Techniques, Protocols, Architectures,
Tools, Applications and Services, Volume 8611, pp 198-216, Springer LNCS, 2014

Conference papers:

  * •

Immich, R. and Cerqueira, E. and Curado, M., “Towards the Enhancement of UAV
Video Transmission with Motion Intensity Awareness”, in the IEEE IFIP Wireless
Days, 2014

  * •

Cerqueira, E. and Quadros, C. and Neto, A. and Riker, A. and Immich, R. and
Curado, M. and Pescapée, A., “A Quality of Experience Handover System for
Heterogeneous Multimedia Wireless Networks”, in the IEEE International
Conference on Computing, Networking and Communications (ICNC), 2013

  * •

Cerqueira, E. and Neto, A. and Immich, R. and Curado, M. and Riker, A. Barros,
H., “A Parametric QoE Video Quality Estimator for Wireless Networks”, in the
GC’12 Workshop: IEEE Workshop on Quality of Experience for Multimedia
Communications (QoEMC), 2012

##  Chapter 7 Mechanisms for Resilient Video Transmission over VANETs

Resilient Video Transmission over VANETs

[George Orwell, Animal Farm]All animals are equal, but some animals are more
equal than others.

V ANETs are envisioned to offer support for a large variety of distributed
applications such as road and traffic alerts, autonomous driving capabilities
and video distribution. The use of video-equipped vehicles, with support for
live transmission, unveils a set of challenges that can range from the scarce
network resources and vehicles movement to the time-varying channel conditions
and high error rates. Here again, to overcome these challenges an adaptive
FEC-based scheme can be tailored to shield the video transmission with QoE
assurance. This chapter details two proposed adaptive mechanisms to improve
the video transmissions over VANETs.

###  27 Introduction

VANET is considered the core component of intelligent transportation systems,
providing support to many applications. The endorsement of video-based
services can be beneficial to a broad range of situations, such as road
safety, driver awareness, traffic status, and infotainment applications.
Besides the users’ experience, the video quality is also important to allow a
better assessment of each situation. For example, it can give police officers,
paramedics, and firefighters an accurate representation of the scene they will
attend, thereby reducing the response time. Beyond the traffic related
services, a sports venue or a festival could broadcast a live feed to incoming
fans caught in a traffic jam. These are only simple examples of a limitless
number of alternatives to make available rich-format services. These services,
however, have to deal with the unreliable wireless connection of the VANETs,
which are highly dynamic in nature and strongly prone to packet loss [Zhou et
al., 2011b, Gerla et al., 2014]. Therefore, it is imperative to strengthen the
video transmissions against losses [Immich et al., 2013b, Immich et al.,
2014c]. This calls for an adaptive mechanism to enhance the video delivery to
provide higher QoE.

In VANETs, there is still a lack of adaptive QoE-driven mechanisms to better
support live video transmissions [Soldo et al., 2011, Shen et al., 2011, Jiang
et al., 2012, Bellalta et al., 2014]. This can be attributed to the
challenging combination of the VANETs’ dynamic topology and the stringent
video requirements. In order to surpass these adversities, a good mechanism
has to take into consideration several aspects of the intrinsic network
characteristics and video details, being able to correctly identify and
protect the most QoE-sensitive data.

As mentioned before, several techniques have been proposed to tackle the
VANETs challenges in the last few years. Some of them are trying to solve
these issues throughout adaptive routing protocols [Marwaha et al., 2004, Zeng
et al., 2013, Pham et al., 2014, Wu and Ma, 2014, Zhang et al., 2015]. The
results show that a reliable routing protocol has a major influence on
improving the video quality. This improvement, however, is restricted to a
specific level. After this level, to increase or even sustain the video
quality it is crucial to resort to some amount of redundant data, which allow
reconstructing the original data set in case of packet losses. A known
approach to supply this redundancy is using FEC techniques. However, due to
the video requirement of a timely delivery of a considerable amount of data
[Zhou et al., 2011a], along with the shared wireless channel resources, a
self-adaptive FEC-based mechanism is advisable. This mechanism needs to have
the capability to operate under unforeseen conditions in order to increase the
human perception while reducing the network overhead.

This chapter proposes and evaluates two QoE-aware mechanisms tailor-made to
overcome any network mishaps, therefore providing the capability to deliver
with high QoE for end-users. The first mechanism described is the adaptive
QoE-driven COntent-awaRe VidEo Transmission opTimisation mEchanism (CORVETTE)
in Section 28 and the latter is the self-adaptive FEC-based proactive error
recovery mechanism to shield video transmissions over VANETs (SHIELD),
detailed in Section 29.

###  28 QoE-driven and Content-aware Video Delivery (CORVETTE)

Owing to the open issues aforementioned, particularly the shortage of adaptive
QoE-driven mechanisms that efficiently consider the motion activity of the
videos together with VANETs features, this section outlines and evaluates the
adaptive QoE-driven COntent-awaRe VidEo Transmission opTimisation mEchanism
(CORVETTE). The proposed mechanism enhances the work described in Sections 24
and 25. The main improvements and new experiment setups are described next.

####  28.1 CORVETTE Overview

The main goal of the CORVETTE mechanism is to improve the resilience of video
transmissions over VANETs. One common issue found in the existing mechanisms
is the lack of QoE-awareness. This means that important video characteristics,
from the human perspective, are neglected, resulting in unnecessary
redundancy. To tackle this issue, the CORVETTE uses a Hierarchical Fuzzy
System (HFS) together with intrinsic VANET characteristics, to add a precise
amount of redundancy exclusively on QoE-sensitive data. This ensures high-
quality video transmission, while downsizing the network overhead.

Figure 45 depicts an overview of the proposed mechanism. The first step is to
assess the network conditions (1). In order to do that, different parameters
are evaluated in a combined way, namely the network density, PLR, and the
node’s position. To calculate the density, first, the network area is found
using a hull algorithm. After that, the total number of 1-hop nodes is divided
by the area, which gives the network density. All these parameters are
necessary because none of them by itself is accurate enough to characterise
the quality of the network links [Vlavianos et al., 2008, Wan et al., 2015].
The combination of them, however, can provide a very good estimation of the
network conditions. Thereafter, using cross-layer techniques, important
details about the video characteristics are collected (2). In the video-aware
procedure of the mechanism, several details are analysed, such as the image
resolution, frame type and size, motion vectors, and macroblock configuration.
At the end, all the gathered data are fed to the fuzzy inference engine, which
will compute a specific amount of redundancy (3).

Figure 45: General view of the CORVETTE mechanism

Provided that the network conditions are not the same at all intermediate
nodes, this parameter has to be reassessed at each hop (4). On the other hand,
the video characteristics do not change during the transmission. In view of
this, they are embedded in each packet header by the server node. This
eliminates the need for processor intensive tasks (e.g. deep packet
inspection) on each and every packet. The IPv6 optional hop-by-hop header was
chosen to store this information [Martini et al., 2007]. This means that it is
always ready to use whenever needed (5,6). Owing to this, the task of
adjusting the redundancy amount on each hop is facilitated. The result is a
higher video quality, and consequently, superior QoE is perceived by the end-
users (7).

####  28.2 Towards the design of CORVETTE

This section describes the manifold procedure and modules that the CORVETTE
mechanism is composed of. Primarily, to enable the CORVETTE real-time
capabilities a knowledge database is needed. The procedure to build this
database uses the same core structure as in the mechanisms described before
with the additional improvements to accommodate the new parameters. A
comprehensive description of this process can be found in Chapters 5 and 6.

An important feature to enable the CORVETTE real-time capabilities is the use
of fuzzy logic. This allows building a dynamic and comprehensive scheme, which
takes into consideration several network and video characteristics while still
manages to perform in real-time. Nevertheless, in conventional fuzzy logic
systems, the rules grow exponentially according to the number of variables.
Therefore, it is common to have a rule-explosion situation when handling a lot
of variables, making the fuzzy logic controller very hard to implement. To
address this issue, the CORVETTE mechanism was designed to use a HFS, where
low-dimensional fuzzy systems can be arranged in a hierarchical form, reducing
the global number of rules because the system grows linearly.

The combined use of the knowledge database and human expertise enables setting
up the fuzzy sets, rules, and hierarchical levels. Figure 46 shows the several
hierarchical levels used by CORVETTE. The output of each low-level component
in the previous layer is used as input to the next layer components. As
mentioned before, on each network hop the amount of redundancy is adjusted
according to several factors, such as the network density, the PLR, and the
distance to the next hop (or final destination). This operation is represented
by the (A) portion of the HFS structure. On the other hand, at the server
node, the full HFS structure is performed, which is composed of the (A) and
(B) parts.

Figure 46: CORVETTE Hierarchical Fuzzy Logic structure

The design of HFS follows the same method as in standard fuzzy logic schemes.
This means that several fuzzy components have to be defined, such as sets,
rules, membership functions and the inference engine. Since this is a complex
process, it has to be executed offline and only once. After loading all the
generated information into the fuzzy engine, it can be used on-the-fly and
with a high performance. A detailed explanation of this process is given
below.

#####  28.2.1 The “General Network” layer conception

The “General Network” component accounts for the definition of the network
health. The characterization of a good or bad channel is not an easy task and
it cannot rely upon a single metric, especially in wireless networks
[Vlavianos et al., 2008]. With this in mind, the CORVETTE mechanism uses three
metrics to better establish a network quality indicator. These metrics are
divided into two specific components, namely “Network status” and
“Communication surroundings”. The former is defined by the combined assessment
of the PLR and network density. The latter is given by the position of the
vehicles. Each one of these metrics is described next.

To compute the network density, the number of nodes is divided by the network
area. Because there is no fixed structure on VANETs and they can quickly
change over time, it is challenging to estimate the network surface area. To
solve this problem, the proposed mechanism uses a convex hull algorithm. In
this type of algorithm, a convex polygon is drawn including all the nodes on
the network. A polygon is convex when it is non-intersecting and a segment
between any two points on the boundary lies entirely inside of it.
Successively, a convex hull is the smallest polygon containing all the points.

There are several algorithms to find the convex hull, however, due to
performance issues, the QuickHull method was chosen [Barber et al., 1996]. It
uses a divide-and-conquer algorithm, and because of that, it is easier to find
the convex hull of small sets, and at the end, the discovered hulls are
merged. Once the convex polygon is found, it is possible to calculate the
surface area and use it to find the network density.

Another component of the “Network status” is the PLR. The main goal in
defining this parameter is to identify the impact of different PLRs in the
QoE. As mentioned before, it is well-known that video sequences have a natural
resiliency to packet loss [Immich et al., 2013a]. To better define the PLR
category, a broad number of network simulations with a large set of video
sequences were carried out. The results show that the QoE is good from the PLR
0% up to 11%. Additionally, in most of the cases, from 5% as far as 22% a
tolerable video quality for end-users was noticed. When exceeding 17% however,
a quick decrease in the video quality was observed, especially in high motion
intensity videos. The majority of the cases, over 34% of PLR, the QoE became
unbearable. Using fuzzy logic it is possible to create classes of PLR exactly
as found in the experiments, even with overlapping values. Algorithm 7.1 shows
the PLR set definition.

1 _InputLVar* PLR = new InputLVar("PacketLossRate");_

2 PLR $\rightarrow$ addTerm( TriangularTerm("LOW", 0, 11));

3 PLR $\rightarrow$ addTerm( TriangularTerm("MEDIUM", 5, 22));

4 PLR $\rightarrow$ addTerm( TriangularTerm("HIGH", 17, 100));

5

6

7 engine.addInputLVar(PLR);

Algorithm 7.1 Packet loss rate input set

The last component of the “General network” layer is the “Communication
surroundings”. In CORVETTE, this component is established by the node
position. This is a straightforward, but very important information. Because
of signal attenuation and radio-frequency interference, nodes further away
from each other tend to require a higher amount of redundancy to preserve a
good video quality. This information becomes even more valuable used in
conjunction with the other input parameters. For example, a much higher amount
of redundancy will be required if the network is very dense and the nodes are
far apart than if the network was not so heavily populated.

#####  28.2.2 The “Video Details” layer conception

Besides the network conditions, the video characteristics also play an
important role when defining a precise amount of redundancy. In CORVETTE’s
hierarchical fuzzy system, the “Video details” criteria layer is divided into
two specific components, namely “Motion activity” and “Video characteristics”.

The motion activity parameter is defined by the combined use of temporal
intensity and spatial complexity. The temporal intensity, in the CORVETTE
mechanism, is given by the motion vector details. This structure is
responsible for tracking the movement of macroblocks from one position, in any
given frame, to another position, in the next one. Since the MPEG standard
allows the use of distinct macroblock sizes, the CORVETTE mechanism adopts the
same procedure of the uavFEC and MINT-FEC mechanisms by computing the area of
each macroblock and using the number of pixels that are being moved. This
enables a better representation of the intensity of the motion in arbitrary
resolutions.

In addition, the Euclidean distance of each motion vector is also computed,
resulting on how far each and every macroblock is being moved. This
information gives more precise results than just counting the number of motion
vectors. All the input parameters are normalised, allowing the protection of
videos with arbitrary resolutions on the fly. Following the same idea as
presented before, an exploratory analysis using hierarchical clustering is
performed to find the best classes that represent the temporal intensity.
After finding the classes, the fuzzy set and the membership function can be
defined. Finding the best-fitted membership function is a complex and problem-
dependent task [Wong et al., 2005], being difficult to attain the optimal
solution. Consequently, piecewise linear functions are desired.

As previously mentioned, the spatial complexity is also used to quantify the
amount of the motion activity. This parameter represents the difference
between the static information that the actual frame has when compared to the
frame before. One way to compute this value is using the Sum of Absolute
Differences (SAD) [Vanne et al., 2006]. This process, however, compares each
and every pixel of both frames resulting in a very complex and time-consuming
operation. Taking this into consideration, the CORVETTE mechanism uses the
normalised frame size to the same end. This enables a much faster operation
and, on top of that, it also allows the use of arbitrary video resolutions.

The same process used to find the different classes in the temporal intensity
is also used to define the clusters here. This means that, once all the frame
sizes are normalised, an exploratory analysis is performed to divide the data
into the most homogeneous groups. After that, using the linkage distance
between the clusters was possible to separate them into five distinct groups,
namely “very small”, “small”, “medium”, “large”, and “very large”, as showed
in Figure 47.

Figure 47: Temporal intensity membership function

With the definition of the fuzzy sets completed, the fuzzy rules must be
designed. As mentioned before, this is an intricate task, which requires a
joint knowledge of the network details, VANETs properties, and video
characteristics. Aiming to reduce this complexity during the design phase of
the rules, as well as to have a better performance in real-time, the CORVETTE
mechanism uses HFS. This layered system allows handling fewer input parameters
at the same time. At the end, the result is a system with a small number of
simple rules, which lead to better performance.

####  28.3 CORVETTE Performance Evaluation and Results

The main goal of the CORVETTE mechanism is to improve the QoE for end-users.
At the same time, it avoids unnecessary network overhead, preserving wireless
resources.

#####  28.3.1 Experiment settings

The simulated scenario is composed of up to 360 vehicles, using IEEE 802.11p
WAVE. The speed range varies from 5 to 17 m/s. Several real video sequences
with three distinct resolutions, namely 1080p, 720p, and SVGA, were used in
the experiments. The videos are examples of regular viewing material, covering
different content and distortions. These sequences also include still and cut
scenes, colour and luminance stress, as well as several motion intensities.
They were all encoded with H.264 codec and GoP length of 19:2.

Using the OpenStreetMAP [Haklay and Weber, 2008], a clipping of 2 by 2 km of
the Manhattan borough (New York City) was obtained. This clipping was used as
input for the Simulation of Urban MObility (SUMO) [Behrisch et al., 2011],
which considers the map structure, driving patterns, routes, crossings,
roundabouts, traffic lights, to generate the mobility traces, which were used
in the NS-3 simulations.

Additionally, in this work, the V2V communication characteristics are explored
to better adjust the proposed mechanism to the actual network conditions. Even
though a VANET environment enables roadside infrastructure, the V2V
communication was chosen because it is unlikely that such infrastructure will
cover all the highways and cities in the near future. Consequently, if the
infrastructure is available it can be used however, the optimisations will
only be performed on the communication between the vehicles.

The adopted routing protocol is the Cross-Layer, Weighted, Position-based
Routing (CLWPR) [Katsaros et al., 2011]. As the name implies, this is a
position-based routing protocol that uses mobility information from the nodes
to tailor itself for VANETs environments. Receiver nodes are using Frame-Copy
error concealment; this means that if a frame is lost, the last good one will
take its place. Table 17 shows the simulation parameters.

Table 17: CORVETTE Simulation parameters Parameters | Value
---|---
Display sizes | 1920x1080, 1280x720, and 800x600
Frame rate mode | Constant
Frame rate | 29.970 fps
GoP | 19:2
Video format | H.264
Codec | x264
Container | MP4
Propagation model | FriisPropagationLossModel
Mobility | SUMO mobility traces
Routing protocol | CLWPR
Wireless | IEEE 802.11p (WAVE)
Radio range | 250-300m
Internet layer | IPv6
Transport layer | UDP
Location | Manhattan borough (New York City)
Map size | 2.000 m x 2.000 m
Vehicles speed | 18-61 km/h (11-38 mph)

Five distinct mechanisms were simulated as stated next: (1) Without FEC, to be
used as a baseline; (2) Video-aware Equal Error Protection FEC (VaEEP), where
I- and P-frames are equally protected; (3) Video-aware Unequal Error
Protection FEC (VaUEP). This mechanism protects I- and P-frames with a
specific amount of redundancy according to the importance of each one; (4) the
adaptive FEC-based mechanism (AdaptFEC), which takes into consideration
several video characteristics and the network state [Immich et al., 2014d]. At
last, (5) the proposed CORVETTE mechanism.

#####  28.3.2 QoE assessments

Figure 48 shows the SSIM results. The first thing to be noticed is that when
the simulation is performed with a few vehicles, e.g., 40, the network is
sparse and suffers from connectivity issues. The CORVETTE mechanism proves
that it can handle those situations by adjusting the amount of redundancy
correctly, thus, outperforming competitors. It is also important to perceive
that the standard deviation is high in all mechanisms. This can be expected
since some video sequences tend to be naturally more resilient to packet loss
than others, resulting in distinctive quality scores. This is especially true
in low motion intensity videos. On the other hand, when the network is very
dense, e.g., above 300 cars, the mechanisms have to deal with interference and
a degraded network connection. Here again, CORVETTE outperforms the other
mechanisms, providing better video quality.

Figure 48: Average SSIM for all mechanisms

Overall, the proposed mechanism exceeds by 48% the without FEC scheme when it
comes to video quality. Against VaEEP and VaUEP schemes, the CORVETTE
mechanism granted over 23% and 19% better scores, respectively. In comparison
to AdaptFEC, the proposed mechanism assured more than 11% higher video
quality. At first, this value does not seem to be very high however, it was
able to achieve it while adding 41% less network overhead.

Figure 49 presents the VQM scores. Almost the same pattern found in the SSIM
metric is also present here. When the network is sparse, the videos tend to
have lower quality, especially without any FEC-based mechanism. The best-case
scenario in the experiments is between 160 and 240 vehicles. In this range,
the videos were transmitted with better quality. Similarly to the SSIM
assessment, using VQM the CORVETTE also outperformed all other mechanisms.
Once again, this proves that the transmission enhancements performed by the
proposed mechanism lead to a better video quality for the end-users. On
average, the CORVETTE mechanism provided 65% better video quality than the
scheme without FEC, 42% and 40% higher scores than VaEEP and VaUEP,
respectively. Against the AdaptFEC scheme, the proposed mechanism presented
30% better results.

Figure 49: Average VQM for all mechanisms

#####  28.3.3 Network footprint analysis

Apart from higher video quality, a lower network overhead is equally
desirable, which is shown in Figure 50. The VaEEP and VaUEP schemes are non-
adaptive, thus producing a constant network overhead through the experiments.
As can be seen in the graph, the non-adaptive protection schemes produce a
large amount of network overhead, especially in the VaEEP case. The VaEEP
protection is also not very efficient because it shields certain parts that do
not require protection. Therefore, the VaUEP scheme takes into consideration
the video characteristics, namely the frame type, and adds a specific amount
of redundancy to each one. This grants the reduction of the network footprint
while improving the video quality. Unlike the VaEEP, the graph shows a
standard deviation for the VaUEP values because different video sequences
require distinctive amounts of redundancy.

Figure 50: Average network overhead

On the other hand, the adaptive mechanisms (AdaptFEC and CORVETTE) yielded
better results in terms of network overhead, allowing a proper usage of the
wireless resources. Another important difference is the standard deviation,
where the CORVETTE mechanism has higher values. In this case, this is very
desirable, because it means that the proposed mechanism was able to better
identify the most important portions of the video sequences and protect them
accordingly. This demonstrated a tailored protection, providing both superior
video quality and low network overhead. On average, the CORVETTE mechanism
added 41% less overhead than AdaptFEC. When compared to the VaEEP and VaUEP
schemes, the proposed mechanism was able to generate 70% and 58% less
overhead, respectively.

#####  28.3.4 Overall results

Table 18 summarises the average SSIM, VQM, and network overhead scores. It
shows that the CORVETTE mechanism was able to provide a substantial reduction
in network overhead. This was possible by adding a specific amount of
redundancy to each video sequences, and thus, avoiding any unnecessary
redundancy. Additionally, it did so while increasing the video quality,
leading to higher QoE for end-users.

Table 18: Average SSIM, VQM, and network overhead | CORVETTE | AdaptFEC | VaUEP | VaEEP | Without FEC
---|---|---|---|---|---
SSIM | 0,876 | 0,785 | 0,734 | 0,709 | 0,591
VQM | 2,266 | 3,264 | 3,783 | 3,935 | 6,425
Overhead | 18,777% | 31,888% | 45,342% | 63,102% | –

Taking everything into consideration, the CORVETTE mechanism showed results
with better video quality while reducing considerably the network overhead.
This is only possible because it performs an accurate motion intensity
classification, which is used along with other video and network
characteristics. The combined use of all this information enables the
mechanism to add an adequate protection to each video sequence improving the
quality for end-users.

###  29 Self-adaptive Mechanism to Shield Video Transmissions (SHIELD)

The CORVETTE mechanism was able to attain encouraging results,
notwithstanding, there was still room for further improvement. For example,
more parameters could be considered to enhance the reliability of the network
conditions assessment, as well as, a reduced number of operations could be
possible by changing the convex hull approach. To address these open issues,
the self-adaptive FEC-based proactive error recovery mechanism to shield video
transmissions over VANETs (SHIELD) was proposed. The main enhancements are
described next.

####  29.1 SHIELD Overview

The aim of the mechanism proposed in this section is to offer videos with
higher QoE to video-equipped vehicles while, at the same time, downsizing the
network overhead footprint. To this end, SHIELD extends the CORVETTE mechanism
by using several video characteristics and specific VANETs details to
safeguard real-time video streams against packet losses. This means that
meaningful video aspects related to the human point-of-view, are not
neglected, which leads to the addition of a very specific amount of
redundancy. One of the main contributions of this work over the CORVETTE
mechanism is the combined used of network density, SNR, PLR, and the vehicle’s
position. This allows SHIELD to better protect the video sequences and enhance
the QoE.

####  29.2 Towards the design of SHIELD

The SHIELD mechanism is an improvement over the CORVETTE work presented in
Section 28. In the same way as presented before, it needs a knowledge database
which is populated before the mechanism execution. Following the same basic
core structure as before, SHIELD also employs a HFS. This allows it to reduce
the global number of rules by arranging them in a hierarchical form, thus,
making it possible to be performed in real-time. Despite the fact that the
same HFS structure was used, all rules and sets regarding the network
parameters were overhauled. On the other hand, the video related details were
kept the same. Figure 51 depicts the hierarchical levels adopted by SHIELD, as
well as the components that were upgraded. The main improvements are detailed
below.

Figure 51: SHIELD HFS structure

There are three layers in SHIELD HFS, namely (A) Objective function, (B)
General criteria, and (C) Specific criteria. The output of each low-level
layer is used as input to the next layer. The first layer (A) represents the
amount of redundancy that SHIELD will add to a specific portion of video data.
The main goal is to find the amount of redundancy for a system that, given its
constraints, results in less network overhead and better QoE. The second layer
(B) encompasses the overall details that the proposed mechanism uses to
determine the redundancy amount, namely the network details and the video
characteristics. The bottom layer (C) is responsible for handling the input
parameters of each feature used by the fuzzy logic inference system. This
layer has a subdivision (C)(2), which is performed at each network hop. All
the input parameters (C)(1) are only taken into consideration at the server
node.

The “General Network” criteria account for the definition of the network
conditions. This component was completely upgraded in the new mechanism. All
the hierarchical rules were rewritten, the input parameters were modified, as
well as a new one was added, namely the SNR. This enables the mechanism to use
more metrics to better establish a network quality indicator. In total, the
SHIELD mechanism uses 4 metrics. These metrics are divided into two specific
criteria, namely “Network status” and “Communication surroundings”. The former
is defined by the combined assessment of the SNR and the PLR. The latter is
given by the network density and the position of the vehicles. Each one of
these metrics is described next.

The SNR is the level of the desired signal against the level of background
noise. This is a good indicator for the physical medium, especially for
spectrum sensing. While this is true, it cannot be considered a reliable
general network quality indicator by itself. This steams from the fact that a
strong channel signal will not always produce a good network connection
[Vlavianos et al., 2008]. On the other hand, a very weak signal will yield a
low-quality network connection. As a result, to create a more holistic
indicator more than one metric has to be used. Another obvious candidate to
define the network quality is the PLR. In general, the SNR and PLR have a
negative correlation, meaning that when one increases, the other decreases and
vice versa. However, they complement each other because the SNR takes into
consideration the physical spectrum part of the transmission and the PLR
provides a point of view closer to the application layer.

The PLR input set was also redesigned. In the same way as before several
experiments were carried out to find the relationship between the losses and
QoE, using a more comprehensive number of video sequences. The results made
evident that it is possible to have a good QoE with packet loss between 0% and
12%, a small difference from CORVETTE’s PLR input set (between 0% and 11%),
yet a step forward to better characterise the network conditions. In most of
the cases, an acceptable video quality for end-users was perceived with losses
from 5% up to 23%. Here again, another small difference in the found values,
which in CORVETTE case were between 5% and 22%. Additionally, after a
threshold of 19% (was 17% in the CORVETTE mechanism) the video quality starts
to decrease apace, particularly in videos with high resolution and motion
intensity. In the experiments with more than 36% (34% in CORVETTE
implementation) of PLR, the QoE reached intolerable levels. Algorithm 7.2
shows only one of the many fuzzy sets defined in the SHIELD mechanism.

1 _InputLVar* PLR = new InputLVar("PacketLossRate");_

2 PLR $\rightarrow$ addTerm( TriangularTerm("LOW", 0, 12));

3 PLR $\rightarrow$ addTerm( TriangularTerm("MEDIUM", 5, 23));

4 PLR $\rightarrow$ addTerm( TriangularTerm("HIGH", 19, 100));

5

6

7 engine.addInputLVar(PLR);

Algorithm 7.2 SHIELD PLR input set

Another component of the “General network” criteria is the “Communication
surroundings”. This component also was enhanced during SHIELD design. It uses
the network density and the position of the vehicles to provide more
information about the network in which the video sequences are being
transmitted. These parameters are updated at each beacon exchange in the
routing protocol. The network density is given by the number of nodes, in this
case vehicles, divided by the network area. It is important to notice that
VANETs are very dynamic networks with a decentralised structure, proving to be
a challenge for the estimation of the network surface area. To address this
issue, the proposed mechanism uses an approximate convex hull algorithm.

A convex hull algorithm is able to find the smallest boundary polygon
containing all the points inside of it, using only non-intersecting segments,
as showed in Figure 52(a). There are several algorithms to find the convex
hull of a given set of points. In the CORVETTE mechanism, the QuickHull method
was used. It uses a divide-and-conquer algorithm with average complexity of
$\mathcal{O}(n\log{}n)$ and at the worst case, it could take
$\mathcal{O}(n^{2})$. However, it is not imperative for the mechanisms to use
high-precision value for the area size, instead, a good approximation is
sufficient to provide very good results. Owing to this, and to improve the
general performance, SHIELD uses the Bentley Faust Preparata (BFP) [Bentley et
al., 1982] approximation convex hull algorithm as showed in Figure 52(b).

|
---|---
(a) Convex Hull | (b) Approximate Convex Hull
Figure 52: Convex Hull and Approximate Convex Hull

The BFP algorithm, which runs in $\mathcal{O}(n)$ time, replaces the sort
operation by dividing the plane into vertical strips. In each strip, the
minimum and maximum points are found and added to the boundary. This algorithm
is an approximation because a non-extreme point, in a given strip, can be
discarded even if it is on the convex hull boundary. Nevertheless, the point
will not be far from the convex hull, resulting in a good approximation of the
actual convex hull.

Figure 53 shows the comparison between the number of nodes and the resulting
amount of operations in both QuickHull and BFP algorithm. On average, the
QuickHull algorithm has fairly good performance, it can degrade, however, up
to exponential in the worst case. On the contrary, the BFP algorithm has a
steady linear performance, providing results more quickly even with a small
number of nodes.

Figure 53: Complexity of QuickHull and BFP

There is a multitude of advantages of performing fewer operations. First of
all, due to the time-sensitive video data, it is important that the client
node receives the information as soon as possible, thus performing fewer
operations allows dispatching the video quickly. In addition, because of the
fast time-varying network conditions, the faster this information is made
available the more accurate it is. At last, performing a minimum number of
operations means less energy consumption as well as more available processor
power to perform other tasks.

Another noteworthy betterment in SHIELD is the approximation of the node
position and the network density inputs. The latter parameter was moved from
the “Network status” component to the “Communication surroundings”. In doing
that, a different set of rules can now be created to better correlate the
movement of the nodes and the number of nodes with 1-hop distance in a given
area. These parameters provide more accurate results when analysed together
than independently, enabling SHIELD to yield a precise network assessment.

In short, the adoption of another input (SNR) and the adjustment of the
“Communication surroundings” component led to a complete rearrangement of the
“General network” module. Which in terms, led better-consolidated rules that
express with higher precision the needs of the proposed mechanism. At the end,
this new “General network” layer presented is responsible for the better
integration of SHIELD in VANETs. In other words, it allows the proposed
mechanism to ascertain the best-fitted QoE-aware amount of redundancy
according to each video sequence.

####  29.3 SHIELD Performance Evaluation and Results

The main goal of the SHIELD mechanism is to enhance the QoE while avoiding any
unnecessary network overhead. In doing that, it improves end-users
satisfaction and preserves the already scarce wireless resources at the same
time.

#####  29.3.1 Experiment settings

In order to better characterise the performance of the proposed mechanism two
distinct environments were assessed: urban and highway. Each of these
surroundings features a variety of unique challenges. In the urban
environment, there are buildings and many other structures that will affect
the signal propagation. On the other hand, in the highway environment, there
is much more free space, which facilitates the signal propagation. Besides
that, the mobility patterns are also very distinctive. The urban scenario
presents a lot of driving options, such as avenues and streets close to each
other. On the highway is quite different, as there are no crossroads and just
a few exits and entrances. In addition, the speed of the vehicles has very
particular properties in each one of these environments. In the urban case,
the velocity usually is between 20 km/h and 60 km/h, and it changes frequently
due to traffic lights, speed bumps, and crosswalks. Meanwhile, on the highway,
the speed variance is very low, generally staying from 80 km/h to 120 km/h.

Several configurations are shared between the two environments, such as the
wireless and network technology (IEEE 802.11p WAVE with V2V and CLWPR routing
protocol), as well as the video content and codification parameters. All
videos were sent using Evalvid Tool and encoded with H.264, GoP length of
19:2. Additionally, three different resolutions were used, namely 1080p, 720p,
and SVGA. For each resolution, 10 videos were chosen to be transmitted
[Xiph.Org, 2016]. A multi-flow scenario was adopted. This means that up to 10
videos are transmitted simultaneously111Samples of the transmitted videos are
available in http://www.youtube.com/channel/UCsB0SdKpCKD2GS6aXzB-FUQ/videos.
All the receiver nodes are enabled with Frame-Copy error concealment.

The mobility traces for both environments were generated using SUMO. For the
urban environment, a clipping of 2 by 2 km of the Manhattan borough (New York
City) was used. This environment was simulated with up to 360 vehicles at
speeds ranging from 20 km/h and 60 km/h. To simulate this environment a
clipping of 10 km of US Interstate Highway 78 (I-78) was used. The number of
vehicles is the same, up to 360, with a velocity between 80 km/h and 120 km/h.

Two different propagation models were used to better represent each
environment. In the highway scenario, the logDistance propagation model was
used [Mittag et al., 2011]. This is because of the open spaces and the reduced
number of sources of interference existent in this environment. This leads to
easier communication between the nodes. On the other hand, in the urban
environment, there are plenty of sources generating interference. In view of
this, the Nakagami-m propagation model was added on the top of the logDistance
model. This allows simulating the fast fading characteristics commonly found
in this environment [Taliwal et al., 2004]. Table 19 summarises the simulation
parameters.

Table 19: Simulation parameters Parameters | Value
---|---
Display sizes | 1920x1080, 1280x720, and 800x600
Frame rate mode | Constant
Frame rate | 29.970 fps
GoP | 19:2
Video format | H.264
Codec | x264
Container | MP4
Wireless technology | IEEE 802.11p (WAVE)
Communication | Vehicle To Vehicle (V2V)
Routing protocol | CLWPR
Mobility | SUMO mobility traces
Radio range | 250m
Internet layer | IPv6
Transport layer | UDP
Highway environment
Propagation model | logDistance
Location | I-78
Map size | 10.000 m
Vehicles speed | 80-120 km/h (50-75 mph)
Urban environment
Propagation model | logDistance + Nakagami-m
Location | Manhattan borough(New York City)
Map size | 2.000 m x 2.000 m
Vehicles speed | 20-60 km/h (12-37 mph)

Figure 54 shows the steps involved in the experiment. First of all, the mobile
traces are required from the SUMO application (1). After that, SUMO will use
real map clippings from the OpenStreetMap (2) to generate the traces. The
traces enable a realistic simulation, providing more accurate results.
Following this, all the information is loaded in the SHIELD mechanism (3).
Next, the proposed mechanism will assess the network conditions (4) and
request the video to be transmitted (5). Real video sequences are used in the
experiment (6). Afterwards, the SHIELD mechanism optimises and secures the
video transmission against packet loss (7). The next step is to deliver the
video sequences to the receiver (8). At the end, the original (9) and the
transmitted (10) videos are assessed using objective QoE metrics (11).

Figure 54: Steps involved in the experiment

Five different scenarios were assessed in both urban and highway environments.
The first one is without any type of FEC. The results of this experiment will
be used as a baseline for the others. The second scenario is the Video-aware
Equal Error Protection FEC (VaEEP) mechanism. The Video-aware Unequal Error
Protection FEC (VaUEP) mechanism is the third scenario. The fourth scenario is
using the adaptive QoE-driven COntent-awaRe VidEo Transmission opTimisation
mEchanism (CORVETTE), presented in Section 28. The fifth and last scenario is
the proposed SHIELD mechanism.

#####  29.3.2 QoE assessments

Figure 55 shows QoE results of the urban scenario. (a) depicts the SSIM
average and (b) shows the QoE improvement in comparison to the base line. In
(a), it is possible to notice that the simulation starts with a few vehicles
and the QoE results, for all mechanisms, can be considered low. This can be
credited to the fact that the network is suffering from connectivity issues
because it is relying on very few and scattered nodes to transmit all the
video data. Even in this scenario, the SHIELD mechanism was able to protect
the most important parts of the video sequences, producing better results. As
showed in (b), this led to an improvement of more than 90% on the video
quality when compared to the baseline (without FEC). The second best result
was the CORVETTE mechanism with 60% of SSIM improvement.

|
---|---
(a) Average SSIM | (b) Percentage of SSIM improvement
Figure 55: SSIM assessment of the urban environment

Figure 55(a) also shows that the best QoE results for all mechanisms are
obtained when the network has 160 and 200 vehicles. This number of nodes
provides the best coverage of the whole area, while it does not cause
excessive interference. Because of the improved network conditions, the
baseline also has better results, thus reducing the SSIM improvement perceived
by the other mechanisms. This situation is clearly evidenced in Figure 55(b)
for 160 and 200 vehicles. On the other hand, when the network becomes very
dense, e.g., above 280 vehicles, the mechanisms have to face increasingly
degraded network connections. Once again, the SHIELD surpassed the other
mechanisms, providing up to 160% higher SSIM scores in comparison to the
baseline.

|
---|---
(a) Average VQM | (b) Percentage of VQM improvement
Figure 56: VQM assessment of the urban environment

Furthermore, Figure 56(a) presents the VQM average and (b) depicts the
percentage of QoE improvement of the mechanisms in comparison to the baseline.
Although this metric differs from SSIM, almost the same pattern can be found
in (a). At the beginning of the experiment, the network is sparse and the
videos have low quality. VQM gives them high scores, which in this case are
not good. This is especially true for the baseline because it does not use any
type of FEC-based mechanism to secure the transmissions. The best-case
scenario in the VQM scores is the same as in the SSIM results, for 160 and 200
vehicles. This confirms the notion that the videos are transmitted with better
quality with this configuration. In the same way as in the SSIM assessment,
the VQM scores demonstrate that the SHIELD mechanism outperforms all other
mechanisms.

Additionally, Figure 56(b) shows a pattern similar to the SSIM results. The
highest improvements are accomplished when the network is sparse, between 40
and 120 vehicles, or in a very dense network, above 240 vehicles. On average,
the proposed mechanism provided scores 78% better than the baseline.
Additionally, it achieved 66% and 63% higher marks than VaEEP and VaUEP,
respectively, and over 40% better scores in comparison to the CORVETTE
mechanism.

In addition to the urban scenario, a highway environment was also assessed
with both SSIM and VQM metrics. Figure 57 shows the QoE assessment, whereas
(a) depicts the average SSIM and (b) shows the percentage of improvement
achieved by the mechanism against the baseline. In (a), the first thing to be
noticed is that the QoE results are closer to one another in this environment.
This happens because the network conditions are not as harsh as in the urban
scenario. At first, there are some connectivity issues when the network is
sparse, e.g., 40 vehicles. After this threshold, a better video quality is
being provided. The best results are evidenced for 120 and 240 vehicles. In
(b), it is possible to notice that the highest improvements are reached when
connectivity issues affect the network, for example, when the number of
deployed vehicles is 40 and 80. In addition, major improvements are also
perceived when there is a higher level of interference, such as above 280
vehicles. Here again, the SHIELD mechanism outperforms all its competitors.

|
---|---
(a) Average SSIM | (b) Percentage of SSIM improvement
Figure 57: SSIM assessment of the highway scenario

The average VQM is shown in Figure 58(a) and the percentage of VQM improvement
by each mechanism is shown in (b). In (a), the results follow the same
tendency as the SSIM scores. This means that the VQM results are also closer
to one another, especially above 120 vehicles. This is evidenced because the
highway environment is not as rough as the urban setting. In (b), it is clear
that the highest percentage of improvement is achieved when the nodes are
sparse. This means that there are connectivity issues in the network, e.g.,
for 40 and 80 vehicles. After this threshold, the network conditions improve
and the enhancements provided by the mechanisms decrease. Nevertheless, the
SHIELD mechanism is able to surpass the competitors.

|
---|---
(a) Average VQM | (b) Percentage of VQM improvement
Figure 58: VQM assessment of the highway scenario

#####  29.3.3 Network footprint analysis

Figure 59 shows the network overhead of all mechanisms in both (a) urban and
(b) highway environments. The non-adaptive VaEEP and VaUEP schemes yield a
constant network footprint in both scenarios because they do not adapt the
amount of redundancy according to the network conditions. As depicted in the
graph, these non-adaptive schemes add a considerably larger amount of
redundancy. On top of that, the protection is not very efficient because, in
the VaEEP case, the protection is added equally to all video data. As
highlighted before, not all video packets need the same degree of protection.
To tackle this issue VaUEP considers the frame type to add a specific amount
of redundancy. This results in less network overhead and, at the same time,
improves the video quality.

|
---|---
(a) Urban scenario | (b) Highway scenario
Figure 59: SHIELD Network overhead

The VaEEP mechanism does not have a standard deviation because it uses a
unique and pre-defined amount of redundancy, which is applied equally to all
videos. The VaUEP mechanism also has a pre-defined amount of redundancy, but
it is not unique. This means that each type of video frame will have a
specific amount of redundancy. Additionally, each video frame has a different
size, leading to a variation in the amount of redundancy, and thus, a standard
deviation is displayed.

The adaptive mechanisms, CORVETTE and SHIELD, were able to produce a lower
network overhead, improving the wireless resources usage. In both mechanisms,
when the network condition is better the footprint decreases. In the urban
environment, this is evidenced when the simulation has 160 vehicles. The
SHIELD mechanism produces a network overhead of only 5%, while CORVETTE is
producing 9%. In the highway environment, the best conditions are experienced
between 120 and 240 vehicles. The overhead produced by the SHIELD mechanism
was between 4% and 7%, while CORVETTE is producing between 8% and 15%.

On average, the SHIELD mechanism added 20% less overhead in the urban
environment and 28% less in the highway scenario, in comparison to the
CORVETTE mechanism. When compared to the VaEEP and VaUEP mechanisms, the
SHIELD mechanism produced 73% and 63% less overhead, respectively in the urban
scenario. In the highway scenario, the network overhead downsize was 81% and
73%, respectively. In the end, the proposed mechanism was able to produce a
tailored protection, enabling a higher video quality and lower network
overhead.

#####  29.3.4 Overall results

The overall results demonstrate that the SHIELD mechanism outperforms all its
competitors as showed in Table 20, which summarises the average SSIM, VQM, and
the network footprint. The SHIELD mechanism enables downsizing the network
footprint in both urban and highway environment. This stems from the fact that
a tailored amount of redundancy, based upon video characteristics and the
network conditions, is added to each video sequence, preventing any
unnecessary redundancy. Furthermore, the proposed mechanism also enhanced the
quality of the video delivered, thus providing higher QoE for the end-users.

Table 20: Average SSIM, VQM, and network footprint | SHIELD | CORVETTE | VaUEP | VaEEP | Without FEC
---|---|---|---|---|---
Urban environment
SSIM | 0,895 | 0,787 | 0,701 | 0,684 | 0,551
VQM | 1,459 | 2,441 | 4,034 | 4,323 | 6,688
Overhead | 17,333% | 21,778% | 46,660% | 65,984% | –
Highway environment
SSIM | 0,911 | 0,809 | 0,744 | 0,729 | 0,627
VQM | 1,328 | 2,095 | 3,414 | 3,728 | 5,281
Overhead | 12,333% | 17,112% | 46,660% | 65,984% | –

###  30 Summary

This chapter described and assessed two proposed mechanisms to increase the
video transmission resiliency over VANETs. The CORVETTE mechanism detailed in
Section 28 enabled safeguarding video delivery over highly dynamic networks,
as well as improving the QoE perceived by end-users. In the VQM assessments,
it was able to surpass the AdaptFEC mechanism presenting results with 30%
higher QoE. When it comes to the SSIM assessment, it generated videos with 11%
higher quality. This value was reached in conjunction with a 41% decrease on
the network overhead, which proves to be a good overall result. Regardless of
the CORVETTE mechanism satisfactory results, there was room for improvement in
the way it handled the network-related parameters.

In order to improve on the aforementioned issues, the SHIELD mechanism,
described in Section 29, was proposed. In the urban environment, SHIELD
achieved between 27% and 57% better QoE in comparison to the CORVETTE
mechanism. On the same scenario, it produced only 17% of network overhead, at
the same time, CORVETTE was producing 22%. In the highway environment, the
SHIELD QoE improvement was between 20% and 87% better than CORVETTE, and also
producing less network overhead, only 12% in comparison to the 17% of
CORVETTE. The assessment results clearly show that the latter mechanism was
able to outperform the other competitors, including the CORVETTE mechanism.
This corroborates that the improvements made to the network module result in
both, better QoE and lower network footprint.

The following publications resulted from the work carried out in this chapter:

Journal paper:

  * •

Immich, R. and Cerqueira, E. and Curado, M., “Shielding video streaming
against packet losses over VANETs”, The Journal of Mobile Communication,
Computation and Information, Wireless Networks, Volume 22, Issue 8, pp
2563-2577, Springer, 2015

Conference papers:

  * •

Immich, R. and Cerqueira, E. and Curado, M., “Towards a QoE-driven Mechanism
for Improved H.265 Video Delivery”, in the 15th IFIP Annual Mediterranean Ad
Hoc Networking Workshop (MED-HOC-NET), 2016

  * •

Immich, R. and Cerqueira, E. and Curado, M., “Adaptive QoE-driven video
transmission over Vehicular Ad-hoc Networks”, in the IEEE Conference on
Computer Communications Workshops (INFOCOM), 2015

  * •

Cerqueira, E. and Curado, M. and Neto, A. and Riker, A. and Immich, R. and
Quadros, C., “A Mobile QoE Architecture for Heterogeneous Multimedia Wireless
Networks”, in the GC’12 Workshop: The 4th IEEE International Workshop on
Mobility Management in the Networks of the Future World (MobiWorld), 2012

##  Chapter 8 Conclusions and Future Work

[Aldous Huxley, Brave New World]As if one believed anything by instinct! One
believes things because one has been conditioned to believe them.

T he video delivery over wireless networks is growing apace, every day
hundreds of thousands of users and companies produce, share, and consume a
vast amount of this type of content. This is an important way to convey
educational information and news reports, as well as the most varied forms of
entertainment. Despite numerous efforts on this field, several open issues
still remain to be solved. This thesis proposed several mechanisms to improve
the quality of the video transmissions in diverse wireless networks
contributing to the advancement of the state-of-the-art in this topic. This
chapter revises the addressed problems emphasising the major contributions
accomplished in this thesis. At the end, future research opportunities and
directions are outlined.

###  31 Synthesis of the Thesis

An effective method for making video transmission resilient to losses is of
critical importance for the success of video transmission over wireless
networks, this is even more evident in multi-hops networks. Due to the recent
technological advances, the end-users of today desire high-quality video
stream, services that do not follow this tendency will have poor acceptance.
However, to guarantee a video transmission with high perceived quality is a
complex task that depends on a multitude of elements.

One of the most evident parameter that has an impact on the video quality are
the network-related details. Taking this into consideration, Chapter 2
presented an overview of the network technologies involved in this thesis. It
started with a review of cross-layer techniques, which are necessary to
provide the flexibility needed to implement any network transmission
optimisation. After that, particular details of each multi-hop wireless
networks are explored, such as the routing protocols and channel
characteristics, as well as the mobility and traffic patterns.

In addition to the network-related features, the video characteristics also
play an important role on how the perceived quality is impaired by losses. In
the light of this, Chapter 3 discussed several video-related peculiarities
that are known to have an impact on the quality, such as the video format, the
type of the frames and the hierarchical structure of the video format, as well
as the motion intensity classification. It is also reviewed how the human
vision system perceives the impairments and the assessment approaches to
quantify the video quality.

Under the given conditions, several mechanisms have been proposed to tackle
the open issues related to the video transmission. Chapter 4 presented the
most relevant ones in the literature. The main characteristics, advantages,
and weakness of each one were discussed. In addition, this chapter also
described the off-the-shelf techniques to improve the video transmission
quality and why they are not enough to ensure best possible perceived quality
to end-users. Owing to the open issues in the literature review the main
contributions of this thesis were proposed.

Chapter 5 introduced three proposed mechanisms to shield video transmissions.
These mechanisms adopted different approaches, such as how to classify the
motion intensity and how to compute the most suited amount of redundancy, to
enable high-quality video delivery over wireless networks. Several techniques
are used to this end, e.g., a heuristic method, random neural networks, and
ant colony optimisation. The results were assessed using quality of experience
metrics, as well as the network footprint impact.

Chapter 6 corresponds to the advancements made on improving video
transmissions using unmanned aerial vehicles. The proposed mechanisms take
into consideration several video characteristics, such as the frame type, the
motion vectors, the spatial video complexity, and the temporal video
intensity, as well as been capable of handling videos with arbitrary size. In
addition, fuzzy logic methods are used to both classify the video details and
to set the redundancy amount. In the same way as before, the experiments were
assessed using quality of experience metrics.

Chapter 7 specified the achievements on enhancing video delivery over
vehicular ad-hoc networks. The mechanisms proposed in this chapter
contemplated both the video characteristics and the intrinsic details of this
type of network, including even the prediction of future packet loss rates.
This was only possible by the combined use of several methods, such as fuzzy
logic and hierarchical fuzzy logic, as well as convex hull and approximated
convex hull algorithms.

###  32 Contributions

The main goals of this thesis were delineated in Chapter 1 and are briefly
revisited here. One objective was to define a method to provide video content
characterization. This should be done according to the motion intensity and
details of each video sequence. Another objective was to propose a series of
adaptive FEC-based content- and video-aware mechanisms. The objective of these
mechanisms is to shield video transmission over wireless networks providing
both high QoE and low network overhead. The third general goal was to study
the performance of the proposed mechanisms in different network environments,
such as WMNs, FANETs, and VANETs.

These goals have tailored the work presented in this thesis and have led to
the following contributions:

Contribution 1, Assessing the impact of the video characteristics on the video
quality level

This is a transversal contribution, which is present in all mechanisms
described in Chapters 5, 6, and 7. The proposed method uses several video
characteristics to assess the impact of them in the perceived video quality.
The video details used were the codec type, the frame type and size, the
length and format of the group of pictures as well as the motion vectors.
According to this information, the proposed mechanisms were able to correctly
identify the most important parts of the video sequences.

Contribution 2, A method to characterise the motion intensity of videos

This is the second transversal contribution in this thesis that is also used
by all mechanisms presented in Chapters 5, 6, and 7. In the proposed method,
the video content is put through a series of procedures to characterise the
intensity pace. This is important because different intensities have distinct
impacts on the perceived quality if some information is lost. The proposed
mechanisms use this knowledge to better protect the most QoE-sensitive data,
which allows providing high perceived quality without unnecessary network
overhead.

Contribution 3, Heuristic mechanism

This contribution was achieved in Chapter 5, Section 19. The main goal of the
proposed mechanism was to provide a practical solution with satisfactory
results. In order to do that, the mechanism used human experience and
knowledge to delineate several strategies to shield the video transmission
against losses. The results show that it was possible to maintain or even
increase the video quality without adding unnecessary network redundancy.

Contribution 4, Random neural networks mechanism

Chapter 5, more specifically Section 20, describe how this contribution was
achieved. In the proposed mechanism, random neural networks were used to both
categorise the video content according to its motion intensity and to set, in
real-time, an adaptive amount of redundancy. The mechanism has a training and
validation phase, which is offline and after that, it is able to perform at
run-time. The QoE assessment shows that it outperform others mechanisms,
generating also, less network overhead.

Contribution 5, Ant colony optimisation mechanism

Section 21 describes the mechanism built to support this contribution. It uses
an ant colony metaheuristic to assess and classify several video
characteristics as well as network conditions, which allows choosing a precise
amount of redundancy. In doing this, the perceived video quality is kept at
the maximum level possible while the network footprint stays low.

Contribution 6, Fuzzy logic mechanism

This contribution is spread in Chapters 6 and 7. Several of the proposed
mechanisms use Fuzzy logic as well as Hierarchical fuzzy logic. These
techniques were broadly adopted because of their flexibility, allowing the
mechanisms to better adapt to a variety of situations. They are also easy to
reconfigure and appropriate to manage abstract concepts. Additionally, it
allows building complex systems, which are able to handle a large number of
inputs at low computation costs. The proposed mechanism’s results show that
they outperform other competitors, producing less network overhead while
increasing the QoE for end-users.

The following subsection outlines further research directions in the fields
addressed in this thesis.

###  33 Future Work

The relevance and the applicability of mechanisms to improve the quality of
video transmissions have been discussed in this thesis, highlighting the need
for reliable solutions. The methods and procedures contemplated in this work
have provided motivating results, as well as showing the ability to outperform
existing solutions in a varied of scenarios. However, there are still several
upgrades that could be addressed in the future including, but not limited to,
concurrent multipath transmission, opportunistic routing, network coding, and
path interleaving.

A distinct aspect that could be addressed is the impact of the proposed
mechanisms in an environment where they need to compete for resources against
other applications.

The adoption of a smart retransmission scheme could also be considered. Such
technique could use a number of information in the decision-making processes,
assessing whether it is worth to retransmit a specific packet. The information
can range from video characteristics, such as frame type, motion activity and
play-out time, and network information, such as delay and link quality, to
FEC-related details, for example, what is the minimum number of packets needed
to reconstruct the original data. In doing that, it would enable the
retransmission of only what is really needed from the user-point-of-view,
respecting the strict constraints commonly found in these services.

Additionally, owing to the ever growing number of wireless-capable devices and
the broader amount of communication technologies that they can use, future
mechanisms will have to accommodate these heterogeneous networks and devices
allowing them to coexist while providing satisfactory levels of user
satisfaction. In multihoming environments, where a single device with several
interfaces can be connected to multiple communication networks, the necessity
of mechanisms to assess the multiple link qualities and adjust the
transmission parameters is even more evident. In this context, wireless mobile
telecommunications technologies such as Long-Term Evolution Advanced (LTE-
Advanced) and 5th Generation Networks (5G) can be used together with Wireless
Local Area Network (WLAN). The mechanisms should consider each communication
channel’s characteristic to adapt the video transmissions. Furthermore, load-
balancing schemes can also be considered to further improve the reliability of
the transmissions.

Along with the wireless communication, the underlying network infrastructure
is also changing. The adoption of Software-defined network (SDN) and Cloud
based systems provides a way to build dynamically adaptable and cost-effective
networks by decoupling the network control from the forwarding functions.
However, one of the main challenges is how to autonomic configure these
networks and protect the transmissions in order to provide the best possible
service while not using unnecessary network resources. In order to achieve
that, several link quality indicators, such as the bit-error rate and signal-
to-noise and distortion ratio, as well as mobility patterns, signal strength,
and the importance of the data that is being transmitted have to be taken into
consideration to programmatically configure the network control layer. This
allows maximising the resources usage in a quick and dynamic fashion leading
to a better transmission quality.

Finally, sustainable computing with energy-aware devices has gained importance
in recent years. The improvement of the overall energy-efficient video
transmission over wireless networks can be accomplished, for example, by
minimising the amount of redundant information, as proposed in this thesis and
thus reducing the network activity. However, there are several other ways to
improve on this issue. In a multihoming environment, for instance, it is
possible to take into consideration the specific wireless technology’s energy
consumption when choosing the more appropriated communication method.
Furthermore, it is also possible to incorporate the end-user preferences and
behaviours to adapt the video transmission mechanisms in order to find the
most desirable energy/quality ratio.

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