# Mobile Internet Experience:
Urban vs. Rural – Saturation vs. Starving?

Anika Schwind, Florian Wamser, Tobias Hoßfeld Würzburg, Germany
{anika.schwind $|$ florian.wamser $|$<EMAIL_ADDRESS>University of Würzburg, Institute of Computer Science Stefan Wunderer,
Christian Gassner Ulm, Germany
{stefan.wunderer $|$<EMAIL_ADDRESS>Nokia Networks

###### Abstract

Mobile Internet experience has been of increasing interest. Services accessed
via smartphone applications shall provide satisfying Quality of Experience
(QoE), irrespective of end user location, time of the day, and other
circumstances. Unfortunately, current LTE networks often don’t provide
constant user throughput, one of the major system influence factors to mobile
Internet QoE. In this paper, we conducted an exemplary measurement study in
LTE networks, comparing the QoE of mobile networks in an urban and a rural
region. Our results show that there are significant differences concerning the
network speed which can result in unsatisfactory service quality depending on
the application to be used. When evaluating the QoE for multiple users who are
using the same base station in a specific area, user satisfaction decreases
drastically, especially in rural areas. Our work encourages for future work to
focus on this gap between the QoE in urban and rural areas.

###### Index Terms:

LTE, throughput measurements, QoE

##  I Introduction and Related Work

Today, the Internet is omnipresent. People are connected no matter where they
are and what they are doing. At home or work, e.g., stable DSL network
connections guarantee a good QoE when using online services. However, people
are often on the move, getting from one place to another by bus, train, or
car, crossing rural as well as urban areas, while expecting a similar Internet
experience from the mobile networks. The QoE can be influenced by several
different factors along the end to end chain. Regarding the network, a major
QoE influence factor is the network speed. Here, the minimum, median, and
current measured download throughput are highly significant in predicting user
satisfaction [1].

As Internet Service Providers (ISP) are interested in measuring their
customers’ satisfaction with their service, it is necessary to monitor the QoE
in their network. An overview of state-of-the-art quality monitoring models
and measuring methods is given in [2]. Here, the authors compare different
approaches and highlight the major challenges for ISPs in ensuring high
service quality. There already exist studies about the current situation in
the mobile networks. For example, [3] conducted large scale QoE measurements
for video streaming in four European countries using a dedicated Docker
container within a distributed testbed. In contrast, [4] used a smartphone
application to measure per-app mobile network performance and evaluated the
results of their crowdsourcing study on the basis of different factors like
connection types (WiFi/cellular). Nevertheless, to the best of our knowledge,
no research exists which explicitly faces the type of area in which the QoE
measurements are taken, e.g., in a densely populated areas or in a more rural
environment.

In this work, a first study was conducted, measuring the mobile network speed
in an urban compared to a rural area. We evaluated the results with focus on
the download throughput and interpreted them according to the resulting QoE of
users of this network. The measurements show that the mobile network provides
sufficient QoE for lightweight applications (Google Maps, WhatsApp, Netflix
video streaming up to HD) in all measured locations, while in the rural area
heavyweight services like 4k video streaming are not possible. When evaluating
the QoE of several competing users, the rural area performs significantly
worse than the urban area. The results show that it is worth to focus on the
difference of the network QoE in urban and rural areas, and that further work
is required to satisfy all users.

The remainder of this work is structured as follows. In Section II, the
methodology to measure the network speed and the location is described.
Section III deals with the measurement data and evaluates the impact of the
maximal available throughput in a given area on the QoE. Finally, Section IV
concludes and gives an outlook on future work.

##  II Methodology

To compare the current available mobile network quality in urban as well as in
rural locations, we conducted a small measurement campaign in two locations in
Germany. Therefore, we ran repetitive network measurements every five minutes.
We used the Docker container MONROE-Nettest [5], with which we were able to
conduct mobile speed measurements and to collect comparable information about
the download and upload data rate as well as the median TCP payload round-trip
time (RTT). The measurements were conducted on a MacBook Air which was
connected to the mobile network using USB Tethering and an Android Smartphone
equipped with a SIM card with unlimited data plan and no speed caps.

For our measurements, two locations were selected. The first one, denoted as
urban area, was in Berlin, Germany. Here, the measurements were conducted in a
mixed residential and business area in the city center having a typical inter-
site distance of 500 m between base stations. The second group of
measurements, further mentioned as rural area, were performed in a hilly,
forested area with very few and small buildings near Cologne. Here, as the
population density is considerably smaller than the density in cities, the
distance between base stations is around 5 km. Both measurements were
performed for more than 24 consecutive hours. For the urban area, 325
measurements were performed from December 11, 2018, at 8:28 pm until December
13, 2018, at 8:23 am. In the rural area, the network speed was measured 371
times in the period from December 16, 2018, at 2:25 pm until December 18,
2018, at 5:53 am.

##  III Measurement Results and Discussions

###  III-A Network measurements: throughput and RTT

Figure 1: Timeline of the measured download throughput

First we consider the measured download throughput over time in the two
locations in Figure 1. The results marked in black are measured in our urban
area, while the results marked in green are measured in our rural area. The
information about the mean, minimum, and maximum value as well as the standard
deviation can be found in Table I. The urban area values fluctuate between
13.3 Mbps and 80.31 Mbps, having a mean download throughput of 45.14 Mbps.
Here, two phases are visible: phase 1 ($ph1$) between 11:33 pm and 6:38 am and
phase 2 ($ph2$) between ph2 7:25 am and 8:23 am. In $ph1$, the measured
download throughput is relatively stable, having a standard deviation of only
1.15 Mbps, and, with a mean of 62.67 Mbps, is very high. Here, the
measurements were conducted at a fixed location at home, having good signal
strength. The traffic during the night was probably low, resulting in a high
constant throughput. In $ph2$, during a long business day, measuring in
various urban locations with wide variations in signal strength and traffic
result in a higher throughput fluctuation, having a standard deviation of
16.18 Mbps. Nevertheless, the mean throughput is still high (26.90 Mbps). In
contrast to that, the measured throughput in the rural area is significantly
smaller, varying between no throughput at all and a maximum of 21.81 Mbps,
having a mean download throughput of 11.44 Mbps. In the rural area, no clear
variation depending on the daytime is visible, probably due to low traffic
variations and due to low signal strength as dominating limiting factor. Thus,
we split the results into two phases according to their fluctuations: phase 3
($ph3$) between 2:25 pm and 1:41 am and phase 4 ($ph4$) between ph2 1:48 am
and 5:53 pm. The standard variation of 5.41 Mbps in $ph3$ is lower than in
$ph2$, but also the mean throughput is much lower. As $ph4$ is relatively
stable, showing only a few peaks, the standard deviation is only 3.78 Mbps.

Not only concerning the download throughput, but also in terms of the upload
throughput and the RTT, the results in the urban area are significantly higher
than in the rural area. Table I shows the statistics of upload throughput and
RTT. The upload throughput (Up T), for example, shows dramatic differences,
having a mean of 23.85 Mbps in the urban and only 0.22 Mbps in the rural area.

TABLE I: Statistics of the collected network measurements | Mean | Min | Max | SD
---|---|---|---|---
Dl T urban (Mbps) | 45.14 | 13.36 | 80.31 | 17.19
Dl T urban ph1 (Mbps) | 62.67 | 60.53 | 65.25 | 1.15
Dl T urban ph2 (Mbps) | 39.45 | 13.36 | 80.31 | 16.18
Dl T rural (Mbps) | 11.44 | 0.00 | 21.81 | 4.88
Dl T rural ph3 (Mbps) | 7.93 | 0.00 | 17.24 | 5.41
Dl T rural ph4 (Mbps) | 12.87 | 0.00 | 21.81 | 3.78
Up T urban (Mbps) | 23.85 | 0.00 | 39.03 | 5.48
Up T rural (Mbps) | 0.22 | 0.00 | 1.42 | 0.32
RTT urban (ms) | 63.35 | 48.53 | 724.55 | 43.57
RTT rural (ms) | 89.26 | 65.73 | 1,833.84 | 92.98
Figure 2: Complementary distribution function of the download throughput

###  III-B User experience: Which applications can be served?

The question arises which apps can be served with high QoE in the urban and
rural areas. To this end, the measured network throughput is compared to the
throughput requirements for different applications yielding a good QoE. Figure
2 shows the empirical complementary cumulative distribution function (CDF) of
the measured throughput in the urban and rural areas. Throughput thresholds
resulting in good QoE for popular smartphone applications are drawn as gray
dashed lines. The required throughput is 4 Mbps for Google Maps and the
messaging application WhatsApp [6]. Netflix provides the following
recommendations for video streaming111Internet connection speed recommendation
for different video resolutions: https://help.netflix.com/en/node/306
(Accessed: 2018-03-08) to avoid stalling: 3 Mbps for SD; 5 Mbps for HD, 25
Mbps for Ultra HD videos. As the throughput in the rural area was very low,
with a probability of 14.29 %, the bandwidth would be not sufficient to use
Google Maps or WhatsApp and providing a good user experience. Having a look at
Netflix video streaming, in 16.71 % of the cases the available bandwidth would
be too low to reach a high user satisfaction for streaming an HD video.
Thinking about 4K videos, which are, for example, used for VR video streaming,
it would be impossible to stream them with high QoE. In an urban area, smaller
apps like Google Maps and WhatsApp can be used without any problem. The same
holds for HD video streaming. Contrary to the rural area, in the urban area in
most of the cases (84.00 %) even streaming a Ultra HD video would be possible
without stalling.

###  III-C User experience: Competing users in mobile networks

Figure 3: Stalling ratio for number of users who simultaneously stream videos

Based on the network measurement results, we conducted now a simulation study
to investigate the user perceived quality when there are several competing
users in the network. To this end, we considered video streaming being one of
the most popular Internet applications in the past decades in the mobile
Internet. According to different subjective studies, e.g., [7, 8], stalling is
one of the KPIs for video streaming. For mobile operators and service
providers, it is important to know whether the bandwidth is high enough to
prevent stalling. As metric we use the stalling ratio $R$ which quantifies the
probability that a video stalls. The stalling ratio is derived analytically in
[9] and depends on the ratio between available network bandwidth $B$ and video
bitrate $V$, $R=1-B/V$. In Figure 3, we calculated the stalling ratio for the
maximum measured bandwidth in the urban 80.31 Mbps (marked in black) and rural
area 21.81 Mbps (marked in green) for different numbers of users
simultaneously watching videos of the same quality. We assume that the
available bandwidth is evenly distributed over all users on average. The
x-axis shows the number of users streaming videos at the same time in the same
cell, while the y-axis indicates the stalling ratio. A clear trend is visible:
For the urban use cases, the stalling ratio is considerably smaller than for
the rural use cases. For example, having a look at SD videos, in the urban
area, up to 26 users can stream a video without stalling, while only 7 users
are served without video interruptions in a rural area. By increasing the
video quality to HD videos, in urban areas 16 users can stream the video
without stalling, while in rural areas the stalling ratio for 16 users would
be 72.73 % and thus, unacceptable.

##  IV Conclusion and Future Work

In this paper, we conducted network measurements in an urban as well as in a
rural area to answer the question whether the mobile network QoE in both areas
is satisfactory. We found that especially the download throughput in the rural
area was significantly lower than in the urban region. This results in the
fact that for one single user, the service quality is sufficient only for
lightweight applications in our rural mobile network. However, the network
usage at busy hour is one factor which can lead to reduced throughput in the
whole end to end chain and thus, can lead to low user satisfaction. This study
should give an incentive for future work to focus this gap between the QoE in
urban and rural areas. Therefore, our measurements should be repeated in a
larger scale to increase the validity and provide more detailed conclusions.

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