# Towards AI-Assisted Sustainable Adaptive Video Streaming Systems: Tutorial
and Survey

Reza Farahani  0000-0002-2376-5802 ,  Zoha Azimi  0009-0007-5405-0643 ,
Christian Timmerer  0000-0002-0031-5243 and  Radu Prodan  0000-0002-8247-5426
Institute of Information Technology (ITEC), Alpen-Adria-Universität
KlagenfurtKlagenfurtAustria<EMAIL_ADDRESS>
(2024)

###### Abstract.

Improvements in networking technologies and the steadily increasing numbers of
users, as well as the shift from traditional broadcasting to streaming content
over the Internet, have made video applications (e.g., live and Video-on-
Demand (VoD)) predominant sources of traffic. Recent advances in Artificial
Intelligence (AI) and its widespread application in various academic and
industrial fields have focused on designing and implementing a variety of
video compression and content delivery techniques to improve user Quality of
Experience (QoE). However, providing high QoE services results in more energy
consumption and carbon footprint across the service delivery path, extending
from the end user’s device through the network and service infrastructure
(e.g., cloud providers). Despite the importance of energy efficiency in video
streaming, there is a lack of comprehensive surveys covering state-of-the-art
AI techniques and their applications throughout the video streaming lifecycle.
Existing surveys typically focus on specific parts, such as video encoding,
delivery networks, playback, or quality assessment, without providing a
holistic view of the entire lifecycle and its impact on energy consumption and
QoE. Motivated by this research gap, this survey provides a comprehensive
overview of the video streaming lifecycle, content delivery, energy and Video
Quality Assessment (VQA) metrics and models, and AI techniques employed in
video streaming. In addition, it conducts an in-depth state-of-the-art
analysis focused on AI-driven approaches to enhance the energy efficiency of
end-to-end aspects of video streaming systems (i.e., encoding, delivery
network, playback, and VQA approaches). It finally discusses prospective
research directions for developing AI-assisted energy-aware video streaming
systems.

Video Streaming; Energy Efficiency; Artificial Intelligence; Quality of
Experience; Content Delivery; Video Networking; HTTP Adaptive Streaming;
Machine Learning.

††copyright: acmlicensed††journalyear: 2024††doi: XXXXXXX.XXXXXXX††journal:
JACM††journalvolume: 1††journalnumber: 1††article: 1††publicationmonth:
6††ccs: Information systems Multimedia streaming††ccs: Computing methodologies
Artificial intelligence††ccs: Social and professional topics Sustainability

##  1\. Introduction

In recent years, the utilization of the Internet has undergone a significant
transformation, with video content now comprising approximately 70 of total
Internet traffic. The Ericsson Mobility Report (Ericsson, 2023) indicates that
over 80 of all mobile data traffic will comprise video by $2028$, exceeding
the current figure of 71. There has been a notable increase in the demand for
video streaming applications among all video traffic. For example, live video
streaming accounts for more than 17 of all traffic in $2023$ (Sandvine, 2024).
This growing demand for streaming applications, particularly after the
COVID-19 pandemic, has intensified society’s dependence on Internet services
and contributed significantly to a 20 increase in the use of Over-the-Top
(OTT) providers (e.g., YouTube, NetFlix) in $2020$ (Conviva, 2020). The rise
in demand, coupled with the support for higher quality and resolution content
on modern smart devices across diverse domains such as entertainment,
broadcasting, healthcare, and education, has motivated OTTs and Internet
Service Providers (ISP) to improve further the user Quality of Experience
(QoE) and network Quality of Service (QoS). These efforts can increase the
energy consumption in the video streaming lifecycle, encompassing content
capturing to rendering, consequently emitting a substantial volume of CO2 and
other Greenhouse Gases (GHG). For this purpose, many initiatives, such as the
Dimpact (Dimpact, 2024) project, involve cooperative efforts of media,
entertainment, and technology companies to diminish the environmental
footprint and enhance the sustainability of the digital media industry.

Table 1. Summary of related surveys.

_Survey_ | _Year_ | _Scope_ | _Video streaming components_ | _AI_ | _Energy_
---|---|---|---|---|---
_Encoding_ | _Delivery_ | _Playback_ | _VQA_
(Ma et al., 2019) | 2019 | Video compression for intra- or inter-frame prediction. | ✓ | ✕ | ✕ | ✕ | ✓ | ✕
(Zhang et al., 2020) | 2020 | Video compression for intra- or inter-frame prediction; video quality assessment. | ✓ | ✕ | ✕ | ✓ | ✓ | ✕
(Shuja et al., 2021; Makhkamov and Khasanov, 2023) | 2021, 2023 | Content caching and prefetching policies. | ✕ | ✓ | ✕ | ✕ | ✓ | ✕
(Saeik et al., 2021; Khan et al., 2022; Feng et al., 2022) | 2021, 2022 | Video compression and transcoding; caching, offloading, and distribution | ✕ | ✓ | ✕ | ✕ | ✓ | ✓
between MEC and clouds.
(Kumar et al., 2022) | 2022 | Scheduling and resource allocation techniques for video streaming. | ✕ | ✓ | ✕ | ✕ | ✓ | ✓
(Sani et al., 2017) | 2017 | ABR algorithm components and their interactions for HAS-based applications. | ✕ | ✕ | ✓ | ✕ | ✓ | ✓
(Barakabitze et al., 2019) | 2019 | QoE modeling, measurement, and control in Software-Defined | ✕ | ✓ | ✕ | ✕ | ✓ | ✓
Networking (SDN) using Network Function Virtualization (NFV).
(Borges et al., 2024) | 2024 | Heuristic and AI-based methods for accelerating video transcoding process. | ✓ | ✕ | ✕ | ✕ | ✓ | ✕
(Afzal et al., 2024) | 2024 | Energy consumption and environmental impact, as well as | ✓ | ✕ | ✓ | ✕ | ✕ | ✓
tools and datasets for encoding, decoding, and displaying video streaming.
This survey | 2024 | Tutorial and survey of AI-based energy-efficient video streaming lifecycle | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
i.e., encoding, decoding, delivery, playback, adaptive algorithms, quality
assessment.

Given the dynamic characteristics inherent in video streaming applications,
reducing energy consumption and CO2 emissions while meeting users’ QoE and
latency expectations poses a considerable challenge. This challenge becomes
more complex by quantifying and analyzing the energy consumption of video
streaming applications, given the extensive data, diverse sequences, devices,
and networks involved. Therefore, recent academic and industrial efforts like
(Xia et al., 2020; Xin et al., 2022; Palit et al., 2023; Ericsson, 2024)
employ Artificial Intelligence (AI) to address these challenges and use its
capabilities to manage large datasets, process structured and unstructured
data, extract complex patterns, and provide predictions and insights.
Therefore, accurately identifying the appropriate stage in the video streaming
lifecycle for applying AI techniques and a comprehensive understanding of the
influential factors is critical.

Recent surveys on AI techniques aimed at improving the energy consumption of
video streaming applications predominantly focus on specific stages of the
streaming lifecycle, such as encoding, delivery, playback, or video
assessment. As summarized in Table 1, works like (Ma et al., 2019; Zhang et
al., 2020) focused on the video encoding part, discussing AI-based video
compression techniques. In contrast, works like (Shuja et al., 2021; Makhkamov
and Khasanov, 2023) studied AI-based in-network techniques such as content
caching and popularity prediction. Other works such as (Saeik et al., 2021;
Kumar et al., 2022; Khan et al., 2022; Feng et al., 2022) surveyed AI-based
solutions to investigate resource allocation and scheduling within the
delivery network segment. In addition, Sani et al., (Sani et al., 2017)
reviewed the literature on different Adaptive Bitrate Streaming (ABR)
algorithms by investigating various techniques for monitoring and measuring
resources such as network throughput, scheduling segment requests to servers,
and adapting the video quality to match resources.

Barakabitze et al., (Barakabitze et al., 2019) presented a review of state-of-
the-art QoE management solutions for multimedia streaming. Their work
integrates modeling, monitoring, and optimization within emerging technologies
and architectures, including cloud and edge computing, and extends to new
domains such as immersive multimedia and video gaming. Borges et al., (Borges
et al., 2024) conducted a survey and categorized heuristic and AI solutions to
accelerate the video transcoding process. Afzal et al., (Afzal et al., 2024)
investigated the energy consumption and environmental impact of video
streaming applications, focusing on the encoding, decoding, and display
components, excluding AI-based solutions. They also discussed different tools
and datasets commonly used in the literature for measuring and analyzing
energy consumption in video streaming applications. However, existing surveys
need a thorough literature review and taxonomy in the entire video streaming
lifecycle to bridge the gap in exploring AI’s contributions to optimizing
energy consumption and QoE. The primary contributions of this survey are
three-fold:

Figure 1. Survey structure.

  1. (1)

Tutorial on video streaming lifecycle, including encoding, delivery, and
playback and exploring metrics and models relevant to video QoE, energy, and
AI techniques employed.

  2. (2)

Taxonomy and survey of $59$ state-of-the-art AI-based energy-aware video
streaming works, covering the entire streaming lifecycle and exploring the
influence of predicting video quality assessment (VQA) metrics on minimizing
energy consumption.

  3. (3)

Future directions in designing AI-enabled energy-efficient video streaming
systems based on the specific lifecycle parts identified in the survey.

Fig. 1 shows the organization of this survey. Section 2 describes the
systematic methodology of the survey, followed by a background on video
streaming, VQA and energy metrics and models, and AI-assisted video streaming
systems in Section 3. Sections 4, 5, 6, 7 provide a taxonomy and survey the
state-of-the-art works in four categories: encoding, delivery network,
playback, and VQA. Section 8 outlines future research directions of AI-driven
energy-efficient video streaming systems before concluding the article in
Section 9.

##  2\. Methodology

This section describes the systematic review methodology used in this survey,
inspired by (of Keele University and Department of Computer Science, 2007)
comprising five _phases (P)_ shown in Fig. 2 (a).

  1. _P_ 1:

_Research question formulation_. We formulated five _research questions (Q)_
by delineating primary areas of the video streaming lifecycle.

    1. _Q_ 1)

_Video encoding:_ How can AI actively contribute to developing content-aware
provisioning, considering both energy and QoE?

    2. _Q_ 2)

_Video delivery:_ How can AI techniques optimize content delivery services
through decision-making policies, e.g., offloading and scheduling to balance
network QoS and user QoE while enhancing energy efficiency and reducing
economic costs?

    3. _Q_ 3)

_ABR algorithm:_ How can AI enhance ABR algorithms by integrating content-
aware information to improve bitrate selection and energy efficiency?

    4. _Q_ 4)

_Video playback:_ How can client-based AI technologies, such as super-
resolution and denoising, optimize video playback while balancing quality
enhancement with energy efficiency?

    5. _Q_ 5)

_VQA:_ How can AI-based techniques reduce dependency on reference frames in
computational VQA processes to optimize energy consumption?

  2. _P_ 2:

_Search and paper collection_. We conducted comprehensive searches in the
scholarly databases and venues listed in Table 2 considering the five research
questions and collected $850$ references.

  3. _P_ 3:

_Relevant paper selection._ In many cases, determining the relevant articles
based on their titles proved challenging. Consequently, we implemented
additional filters by scrutinizing the abstracts and meticulously reviewing
papers that met the following criteria: (1) published after 2015, indicative
of a significant trend in the research landscape; (2) sourced from journals
with high impact factors or conferences ranked B or higher according to the
_CORE_ ranking pertinent to the field (Table 2); and (3) authored by
individuals affiliated with universities ranked in the top $500$ of the _QS
World University Rankings_ or companies listed in the _Fortune 500_.

  4. _P_ 4:

_Keyword analysis and taxonomy development._ We used $100$ keywords from the
video, energy, and AI domains to conduct a detailed analysis and formulate a
taxonomy for AI-assisted sustainable systems, classified into four categories:
encoding, delivery, playback, and VQA.

  5. _P_ 5:

_Information extraction and literature review._ We extracted information from
the selected works to address the five research questions outlined in the
first phase. We contacted the authors to gain more in-depth information about
their proposed methodologies and experimental designs when required. From the
initially selected $850$ articles, we thoroughly surveyed $210$ articles,
referencing $59$ articles that directly or indirectly use AI-based energy-
aware solutions for the video streaming lifecycle (Fig. 2 (b)).

Figure 2. (a) Systematic literature review methodology, and (b) distribution
of surveyed articles based on video streaming lifecycle segments across
different years. Table 2. Search strategy and sources in the systematic
literature review.

Year | $20182024$
---|---
Keywords | Video |  |  General: Video, video streaming, VoD, live, HTTP adaptive streaming, MPEG DASH, Apple HLS, AVC, HEVC,
---
VVC, AV1, VP9.
Encoding: Entropy encoding, transform coding, intra-, inter-frame, motion
estimation/compensation, CU, PU,
CTU, partitioning, block partitioning, per-title encoding, per-scene encoding,
transcoding, transrating.
Delivery: Video transmission, network-assisted streaming, CDN, edge computing,
task scheduling, MEC, CDN,
computing continuum, WebRTC cloud computing, content delivery, resource
allocation, P2P, D2D, TCP, QUIC,
MPTCP, content caching.
Playback: video decoding, ABR, quality enhancement techniques, super-
resolution, frame interpolation,
denoising, display.
VQA: VMAF, SSIM, PSNR, QoE, latency, P1203, video quality assessment, stalling
events, rebuffering,
quality switch.
Sustainability |  | Energy consumption, power consumption, battery, battery life, CO2, carbon footprint, green, sustainable,
---
energy efficiency.
AI |  | Machine learning, supervised learning, unsupervised learning, decision tree, random forest, SVM, LLM,
---
prediction, classification, deep learning, CNN, LSTM, reinforcement learning,
Q-learning, online learning
deep reinforcement learning, generative AI, explainable AI.
Venues | Encoding, Playback, |  IEEE: TCSVT, TIP, Broadcasting, TMM, Access, DCC, ICME, MMSP, ICIP, VCIP, QoMEX.
and VQA |  ACM: MM, MMM, TOMM.
| Delivery |  |  IEEE: TNSM, TMC, Access, TPDS, ICC, ISM, LCN, GLOBECOM, INFOCOM, JSAC, TVT; USENIX: NSDI, ATC;
---
ACM: SIGCOMM, CoNext, MMSys; Elsevier: Computer Networks, Computer
Communications.
| Surveys |  IEEE: COMST, Access, TCSVT; ACM: CSUR, TOMM; Elsevier: Computer Networks, SUSCOM.
Scholarly search engines | Google Scholar, IEEE Xplore, ScienceDirect, ACM Digital Library, ResearchGate, arXiv.

##  3\. Background

This section provides basic background knowledge on video streaming, delivery
networks, VQA, and energy metrics and models. It also presents an overview of
the AI-assisted techniques commonly employed for video streaming applications.

###  3.1. Video Streaming

Video streaming requires simultaneously downloading and playing back audio and
video content, following the lifecycle in Fig. 3. In contrast to traditional
video downloading, which retrieves the entire video sequence before playback,
video streaming enables users to start watching almost instantly during video
transmission. _HTTP Adaptive Streaming_(HAS), e.g., based on Moving Picture
Experts Group (MPEG) _Dynamic Adaptive Streaming over HTTP_ (DASH)
standardization (MPEG-DASH, 2017) or Apple _HTTP Live Streaming_ (HLS) (HLS,
2024), is nowadays the predominant video streaming technique (Yin et al.,
2015; Yaqoob et al., 2020). HAS-based streaming encodes the video content in
multiple quality (e.g., bitrate and/or resolution) levels, known as
representations, and divides them into short and fixed-duration segments. A
collection of these representations, known as the _bitrate ladder_ , is stored
on the media server, such as the origin server or Content Delivery Network
(CDN). A manifest file, such as the HLS playlist or DASH MPD, contains the
representation information, including the addresses (i.e., HTTP Uniform
Resource Locators (URLs)) of the segments stored on media servers. When the
video player issues a streaming request, it loads this manifest file, analyzes
it, and selects an appropriate segment representation. Determining proper
representations depends on network throughput and player characteristics, such
as buffer status and display resolution. Therefore, the client must
continuously monitor these metrics and dynamically adjust the video quality by
choosing the most appropriate representation for the next segment using an ABR
algorithm to provide a smooth streaming session with a high network QoS and
user QoE (Herglotz et al., 2022; Hossfeld et al., 2023).

###  3.2. Delivery Networks

The delivery network includes an origin server with one or a hybrid
combination of three networks, as shown in Fig. 3.

####  3.2.1. Content Delivery Network (CDN)

It is a distributed network of servers that work together to deliver content
to users, including three main components (Pathan et al., 2007; Nygren et al.,
2010):

  1. (1)

_CDN servers_ , also known as surrogate, cache, or edge servers, placed in
various geographical locations near end-users, cache and deliver content
quickly and efficiently. A user request contacts the closest CDN server, and
if the CDN server has the content in its cache, it fulfills the request faster
than retrieving it from the origin server. Otherwise, the content is fetched
from the origin server and served at rates slower than delivery from the CDN
servers.

  2. (2)

_Origin server_ is responsible for storing and updating the original content.
When a CDN server does not cache the requested content, the origin server
serves the request and then feeds the CDN servers for future demands based on
the content distributor policy.

  3. (3)

_CDN content distributor_ manages content distribution across the CDN and
defines important policies for improving the network performance. For example,
it monitors the CDN network to detect frequently accessed content, which it
caches on CDN servers to reduce the load on the origin server and improve the
overall content delivery performance.

Figure 3. General video streaming life cycle, including video encoding,
content delivery, and playback phases.

####  3.2.2. Multi-access Edge Computing (MEC) network

As a complementary technology to cloud computing, provides storage and compute
resources close to end-users at the network’s edge with lower data processing
latencies, better user experience, and lower cost and bandwidth consumption
(Taleb et al., 2017). MEC can offer several merits in various types of video
streaming applications, including:

  1. (1)

_VoD streaming._ Applying techniques such as most popular content caching or
content prefetching in advance at the edge enhances the scalability and
reliability of VoD services, while reducing the load on cloud servers (e.g.,
origin server), enabling faster content delivery and boosting user QoE
(Aguilar-Armijo et al., 2023).

  2. (2)

_Live streaming._ Caching, transcoding, and processing content closer to the
user who generates and consumes the data reduces latency and improves live
video streaming quality (Farahani et al., 2022c; da Mata Liborio Filho et al.,
2024).

  3. (3)

_Immersive video streaming._ Augmented Reality (AR) or Virtual Reality (VR)
applications necessitate real-time processing and rendering of content to
reduce latency and enhance performance. Thus, they demand advanced processing
services and techniques beyond traditional streaming, such as real-time data
analytics at the network edge closer to the users.

####  3.2.3. Peer-to-Peer (P2P) network

It is a decentralized delivery where each peer device communicates and shares
content directly with others rather than relying on a centralized server. Each
peer in a P2P network acts as both client and server that simultaneously
downloads and uploads content (Siano et al., 2019). A typical P2P network
includes three peer types:

  1. (1)

_Tracker server_ records peers’ logs and coordinates content exchange for
efficient distribution and synchronization. When a peer requests content, it
contacts the tracker server to fetch a list of others holding the content for
direct download without relying on a central server.

  2. (2)

_Seeders_ are peers who completely downloaded and shared the content with
others. Once a peer downloads the content from a seeder, it may become a
seeder and continue sharing. Holding high resources, e.g., upload/download
bandwidth and battery, is another characteristic of seeders, which allows them
to remain connected to the P2P network for extended periods; consequently,
other peers fetch their desired content from these stable peers.

  3. (3)

_Leechers_ are peers still downloading the content and have not yet fetched it
completely.

###  3.3. Video Quality Assessment (VQA)

Efforts to reduce the energy consumption of video streaming applications must
primarily preserve the users’ QoE. Therefore, several standards and frameworks
from the literature evaluate the quality of video sequences received by end
users through QoE metrics and models.

####  3.3.1. VQA metrics

They measure and assess the perceived quality of videos, facilitating
comparisons between different techniques, and typically fall into two groups.

  1. (1)

_Subjective VQA metrics_ relate to human perception. Mean Opinion Score (MOS)
(ITU-T, 2020b) a popular metric that evaluates video QoE based on user
opinions on a scale of $15$ (highest quality).

  2. (2)

_Objective VQA metrics_ use different algorithms to predict and quantify human
perception based on parameters affecting the quality of a compressed video.

     * •

Peak Signal-to-Noise Ratio (PSNR) (Hore and Ziou, 2010) reports the mean
squared error of differences in pixels of an original (signal) and compressed
or distorted (noise) video. The PSNR is typically in the range of 3050 for
$8$-bit data, and 6080 for $16$-bit data(Deshpande et al., 2018), where a
higher value indicates a better quality. XPSNR (Helmrich et al., 2020) extends
naive PSNR for ultra-high-resolution content.

     * •

Structural Similarity (SSIM) (Wang et al., 2004) rather than using pixel
information, computes the correlation of neighboring pixels and the match of
the luminance and contrast of the compressed and original videos in the range
$-11$ (where $1$ shows perfect similarity) (Lee et al., 2017; Luo et al.,
2019).

     * •

Video Multi-Method Assessment Fusion (VMAF) (Netflix, 2016) developed by
Netflix extracts spatial, temporal, and human perceptual features from
distorted and reference video frames. It uses such features as inputs to a
Machine Learning (ML) model to predict the subjective quality that minimizes
the difference to human judgments. VMAF ranges from $0100$, where a higher
score indicates better quality (Netflix Technology Blog, 2016).

####  3.3.2. VQA models

VQA metrics do not consider some of the influential factors significantly
affecting viewer satisfaction (Barman and Martini, 2019) such as:

  * •

_Rebuffering/stalling_ interrupts or pauses video playback when the player
runs out of buffered data and needs to load more to continue playback.

  * •

_Quality switches_ alterations in visual resolution or clarity of a video
presentation.

  * •

_Startup delay_ represents the time lapse between initiating the playback
request and the actual display of the video on the screen.

In recent years, several QoE models have been introduced to consider and
incorporate these influential factors. The most well-known models are:

  1. (1)

_ITU-T P.1203_ introduced by ITU (ITU-T, 2020a) has three modules for
estimating video and audio quality and audiovisual integration. This objective
model quantifies QoE on a scale of $15$, where a higher score indicates a
higher QoE. The video quality estimation model has four operation modes with
incremental access to the input information:

     * •

Mode 0 accesses the codec, target bitrate, display resolution, framerate, and
segment duration information;

     * •

Mode 1 considers frame sizes and frame type information in addition to input
from mode 0;

     * •

Mode 2 receives up to 2 of bitstream information;

     * •

Mode 3 has access to all bitstream information on top of all the inputs of
previous modes (Robitza et al., 2018).

  2. (2)

_Yin_ et al., (Yin et al., 2014) evaluates individual video segments using a
weighted sum of video quality, quality switches, and total rebuffering time.
Numerous studies, (Bentaleb et al., 2022; Belda et al., 2020) expanded its
scope to include additional influential VQA factors, such as playback speed.

###  3.4. Energy Quantification

Efforts to quantify energy consumption and carbon emissions have gained
significant momentum in recent years. Several works from the literature
present metrics and models to measure and predict energy use and emissions
across the video streaming lifecycle.

####  3.4.1. Energy Metrics

The most common metrics used for measuring energy consumption are:

  * •

Power consumption measures the amount of electrical power consumed during
video streaming in (Yue et al., 2022).

  * •

Energy consumption measured in or is the total streaming energy used, usually
by multiplying the power consumption with the video duration (Li et al., 2021;
Bentaleb et al., 2023).

  * •

Energy efficiency is the ratio between video quality (e.g., resolution,
bitrate) to the energy consumed, such as [per-mode=symbol] or
$\mathrm{Pixel/W}\text{/}$.

  * •

Battery life assesses the duration for each device (e.g., mobile or tablet)
maintaining video streaming functionality, typically in hours, before
recharging.

  * •

Carbon intensity represents the average grams of CO2 equivalent emitted per
$\mathrm{kW}\text{\,}\mathrm{h}\text{/}$ of electricity generated with
significant variation between countries and energy sources.

  * •

Carbon footprint is the electricity consumption multiplied by the carbon
intensity, reported as the total amount of GHG expressed in tonnes or of CO2
equivalent (CO2e) (Stephens et al., 2021).

####  3.4.2. Energy models

Several recent energy consumption and carbon emissions models analyze and
predict the impact of video streaming applications. Each model focuses on a
specific stage of the video streaming lifecycle rather than adopting a
comprehensive end-to-end approach.

  1. (1)

_Encoding models_ formulate or predict the energy requirements of compressing
video sequences using various codecs and parameters. Sharrab et al., (Sharrab
and Sarhan, 2013) introduced a linear model for encoding that specifically
addresses the AVC codec utilized in live streaming systems incorporating
encoding parameters, such as the number of reference frames, motion estimation
range and algorithm, and Quantization Parameter (QP). Azimi et al., (Azimi
Ourimi et al., 2024) proposed an Extreme Gradient Boosting (XGBoost) model to
predict the energy consumption of the encoding process by utilizing features
such as video complexity, QP, resolution, framerate, and codec type.

  2. (2)

_Delivery models_ formulate or predict the energy required for preparing and
transmitting video sequences across delivery networks to end users. Huang et
al., (Huang et al., 2012) proposed a linear model for LTE networks based on
the uplink and downlink throughput. Mehrabi et al., (Mehrabi et al., 2019)
modeled the energy consumption of an edge server for transcoding tasks in a
time-slotted manner based on the data volume processed for all active clients.
To this aim, they considered specific bitrate allocation to each client,
completed transcoding tasks, a processing weight factor for each edge server,
and the energy consumption per bit of data, accounting for interactions with
other edge servers and the overall processing load.

  3. (3)

_Playback models_ formulate or predict the energy required to process,
decompress, and playout content on user devices. Herglotz et al., (Herglotz et
al., 2015) introduced a linear regression model to predict energy consumption
based on decoding processing time, enabling measurements independent of
hardware and operating systems. Turkkan et al., (Turkkan et al., 2022)
proposed an Artificial Neural Network (ANN) model to predict the power
consumption of a video sequence during playback based on parameters such as
bitrate, resolution, framerate, and file size.

Figure 4. General AI-assisted video streaming workflow.

###  3.5. AI-assisted Video Streaming

The typical pipeline of an AI-based video streaming system includes four
stages, which can apply to one or multiple parts of the video streaming
lifecycle (Fig. 4):

  1. (1)

_Raw input data_ encompass details of the video sequence (e.g., video
complexity features), network (e.g., throughput, load), CDN statistics (e.g.,
cache occupancy, request popularity), and end-user devices (e.g., playback
state, buffer level, battery status) (Farahani et al., 2023a, 2022b). To
address communication challenges among clients, the origin server, and the
CDNs, the Consumer Technology Association (CTA) (CTA, 2024a) in the Web
Application Video Ecosystem (WAVE) project has introduced the Common Media
Client Data (CMCD) (CTA, 2024b) and Common Media Server Data (CMSD) (CTA,
2024c) specifications that standardize the streaming components to convey the
information, ensuring consistent data interpretation and analysis.

  2. (2)

_Feature extraction_ involves identifying, capturing, and selecting relevant
characteristics within the input data, including recognizing motion patterns,
identifying recurring themes, and noting variations in the resolution of the
video sequence. To facilitate this feature selection process, statistical
models (Khan et al., 2009; Seufert et al., 2019) or machine learning
algorithms (Yu et al., 2021) are often applied, ensuring that the model
prioritizes crucial elements to improve the desired output, e.g., video
streaming quality and user satisfaction.

  3. (3)

_Learning_ involves training models to discern patterns within existing data
and determine an appropriate decision-making action. This could entail
_Reinforcement Learning_ (RL) (Farahani et al., 2022a; Bentaleb et al., 2023),
classification using methods such as Support Vector Machines (SVM) (Zhang et
al., 2015, 2017), or prediction through techniques like Long Short-Term Memory
(LSTM) (Menon et al., 2023; Zheng et al., 2021) or Random Forest (RF)
(Amirpour et al., 2023; Menon et al., 2024a). The initial step divides the
dataset into training and testing sets, with subsequent training of models on
the designated training set. The model parameters are adjusted based on the
features extracted in the previous stage, facilitating the understanding of
the underlying patterns. To assess the model’s efficacy on the test set,
relevant evaluation metrics such as Mean Absolute Error (MAE) or Mean Squared
Error (MSE) are defined (Amirpour et al., 2023; Menon et al., 2024a).

  4. (4)

_Inference_ utilizes the learned patterns and information to make decisions,
predict, or classify unseen data associated with encoding, content delivery,
playback, or VQA.

##  4\. Video Encoding

The encoding phase in video streaming compresses video sequences, facilitating
efficient storage and seamless data transfer across the delivery network. This
involves eliminating redundant data from raw input files or high-quality
encoded video sequences and retaining only essential information. Various
compression-decompression technologies, known as codecs, such as Advanced
Video Coding (AVC/H.264 (ITU-T, 2021; Wiegand et al., 2003)), High Efficiency
Video Coding (HEVC/H.265 (ITU-T, 2023a; Sullivan et al., 2012)), AOMedia Video
1 (AV1) (Han et al., 2021), and Versatile Video Coding (VVC/H.266 (ITU-T,
2023b; Bross et al., 2021)), incorporate distinct compression modules (refer
to Section 4.2 for further information). Optimizing the encoding parameters by
modules within these codecs reduces the data to encode, subsequently reducing
the encoding time and computation complexity. This, in turn, contributes to
energy efficiency in computation.

###  4.1. Encoding Configuration

Encoding configuration involves the choice and optimization of encoding
parameters, in some implementations (Wieckowski et al., 2021; MulticoreWare,
2024) known as “preset”, varies from very slow to ultrafast. Faster presets
achieve faster encoding with less compression efficiency, while slower presets
obtain higher compression efficiency at the cost of longer encoding time and
more energy consumption (Laude et al., 2019). In addition, several other
parameters influence compression efficiency, including:

  * •

_Bitrate_ is the amount of data transmitted per unit of time, expressed in
bits per second (bps).

  * •

_Framerate_ is the number of individual frames displayed per second in a
video, expressed in frames per second (fps).

  * •

_Resolution_ is the number of pixels in a video frame, expressed as the width
and height.

  * •

_Quantization parameter (QP)_ balances video quality and compression
efficiency by deciding which details to retain or discard during encoding
process.

Conventional methods often rely on predetermined presets, independent of the
video content and not tailored to the specific characteristics of video
content, proving inefficient for video sequences with unique features (Azimi
et al., 2024). Video content featuring a high level of dynamism, characterized
by rapid scene changes and fast-paced actions, differs significantly from
static content. There are several works on customizing settings for each video
title or scene, referred to as per-title and per-scene encoding, respectively.

####  4.1.1. Per-title encoding

introduced by Netflix investigates the characteristics of video sequences for
finding the “best” encoding parameters balancing quality and compression
efficiency based on their complexity (De Cock et al., 2016), encompassing
scene changes, motion, and content variability. For the content of average
complexity with a $1080$p resolution, per-title encoding allocates 20 less
bitrate compared to the fixed ladder method (Netflix Technology Blog, 2015).
Although the impact of this method is significant in bitrate savings, it tends
to be slower and more resource-intensive (Dahl, 2018). Therefore, several
research works are trying to improve the efficiency of per-title encoding,
exploring AI-based approaches that leverage video feature extraction and
optimized encoding parameter prediction capabilities. Fig. 5 depicts an
example workflow of AI-based per-title encoding that starts with live or VoD
video source, including three key stages:

  1. (1)

_Content complexity analysis_ considers information like:

Figure 5. AI-based per-title encoding for two video sequences with distinct
complexities.

(a) spatial information, referring to the arrangement and distribution of
pixels in video frames, including colors, shapes, textures, luminance,
chrominance, and patterns;  (b) temporal information, relating to the
variations over time in a video and capturing motion information and dynamics
across frames.

  2. (2)

_AI-based predictors_ uses ML and ANN-based models (e.g., Gaussian Process
(GP) (Katsenou et al., 2021), RF (Menon et al., 2024a), and (Falahati et al.,
2024)) to estimate the optimized encoding profile based on the complexity
analysis.

     * •

_QP prediction._ Katsenou et al., (Katsenou et al., 2021) proposed a content-
driven method that extracts temporal and spatial features from the
uncompressed content for each video sequence. It then applies a GP model to
predict the QPs, leading to an optimized bitrate ladder that saves 89.06 of
the required encodings compared to the exhaustive search method, leading to
less processing time and energy consumption.

     * •

_Bitrate ladder prediction._ Mueller et al., (Mueller et al., 2022) proposed
an encoding method using a combination of three ML models (i.e., XGBoost,
Convolutional Neural Networks (CNN), and fully connected neural networks to
extract features from a video sequence based on its characteristics. They
proposed a bitrate ladder that saves up to 20 bitrate and reduces the carbon
footprint of video streaming compared to conventional per-title encoding.

     * •

_Preset prediction._ Amirpour et al., (Amirpour et al., 2023) used an RF model
to predict the optimized preset based on the brightness and temporal
complexity of $500$ video sequences for an HEVC encoder, showing 70 energy
savings with a penalty of only $0.15$ in the VMAF score.

     * •

_Resolution prediction._ Menon et al., (Menon et al., 2024a) introduced an RF-
based prediction of the video encoding time and XPSNR, allowing dynamic
resolution adjustments based on video segments’ spatial and temporal
characteristics. The approach achieved a bitrate reduction of 12.03 within a
200 encoding time constraint with an impressive 84.17 decrease in encoding
energy compared to the reference HLS bitrate ladder. Azimi et al., (Azimi
Ourimi et al., 2024) employed an XGBoost model to predict and balance encoding
energy and VMAF scores using spatial and temporal complexity features,
resolution, framerate, QP, and codec. They used predictive models to identify
optimal encoding parameters (i.e., resolution and QP) for each video sequence,
balancing energy consumption and VMAF. The approach achieved an average energy
reduction of 46 with only a four-unit decrease in VMAF.

  3. (3)

_Video encoding_ considers the content characteristics of each video by
adjusting parameters obtained from the previous stage. After encoding, the
convex hull, which encapsulates the bitrate-resolution pairs with their
corresponding VMAF scores, ensures that the selected combinations lie close to
this convex shape. Fig. 5 shows two video sequences with distinct VMAF scores
for identical bitrate and resolution values, underscoring the importance of
different bitrate ladders for each video content based on its complexity.

####  4.1.2. Per-scene encoding

It analyzes video sequences scene-by-scene and determines the best encoding
parameters on their complexity and characteristics. Similar to per-title
encoding, AI could be influential in analyzing the video sequence and
extracting the features of each scene. For instance, (Seeliger et al., 2022)
used the Deepencode model (Mueller et al., 2022) to categorize the scenes in a
video sequence based on their complexity and predict the best bitrate
accordingly, which reduced the energy consumption of HD and UHD video by 9 and
30, respectively.

###  4.2. Redundancy Reduction

Redundancy reduction minimizes the data necessary to represent video frames.
Each video codec employs a set of modules. Since video sequences encompass
both spatial and temporal features requiring compression, recent video coding
standards incorporate hybrid video coding techniques covering both spatial and
temporal aspects (Li et al., 2020), including:

  1. (1)

_Predictive coding_ eliminates unnecessary video sequence information by
addressing spatial and temporal redundancies utilizing intra- and inter-frame
prediction methods. Higher prediction accuracy results in encoding fewer
residuals, i.e., differences between actual and predicted frames, leading to
enhanced compression efficiency and energy consumption.

  2. (2)

_Transform coding_ shifts residuals to a spectral domain. It then quantizes
the spectral coefficients to diminish spatial and perceptual redundancies.

  3. (3)

_Entropy coding_ addresses statistical redundancies by encoding symbols with
higher probabilities using fewer bits and symbols with lower probabilities
with more bits.

Many state-of-the-art codecs and works leverage advanced predictive
compression techniques, including intra-frame and inter-frame coding and
motion compensation.

####  4.2.1. Intra-frame prediction

As part of the predictive coding process, it segments video frames into codec-
specific block sizes that play a pivotal role in video compression. For
example, in the HEVC standard, these blocks called Coding Tree Units (CTU)
have dimensions of ($16\times 16$), ($32\times 32$), and ($64\times 64$),
further broken down into Coding Units (CUs) and in turn subdivided into
smaller Prediction Units (PU) (Çetinkaya et al., 2021). The intra-frame
prediction uses encoded samples within the same video frame to estimate the
pixels within each PU. Fig. 6 illustrates an example of the HEVC CU
partitioning process, representing an AI-based classification task that
analyzes the input video frame to extract the most relevant features, such as
temporal, spatial, and motion information, fed to an AI-based classifier. The
classifier determines whether to split a specific CU level based on the
similarity of each region’s features. Various intra-prediction modes, e.g.,
horizontal, vertical, and angular (Nair et al., 2019; Laude and Ostermann,
2016), can identify the optimized mode with the minimum error (Sullivan et
al., 2012; Zhang et al., 2017). In all studies, decisions, such as whether to
split a coding unit (Mercat et al., 2018), determine the size of the block
(Zhang et al., 2017), and select the intra-prediction mode (Jillani et al.,
2020) constitute integral aspects of intra-frame prediction.

Figure 6. AI-based block partitioning example in HEVC codec.

Zhang et al., (Zhang et al., 2017) proposed an ML model for data-driven CU
size decisions to address the challenges of encoding complexity and power
consumption of HEVC intra-frame prediction. Their model consists of an SVM
classifier to determine whether to split the CU or propose an uncertain
condition for situations requiring further investigation, aiding in the
prevention of false predictions. During instances of uncertainty, a second
stage of binary classification is performed, leveraging insights from
previously coded frames to enhance decision-making. The results show an
average reduction in computation complexity of 52.48 compared to the original
HM $16.7$ encoder, resulting in significant energy savings.

The authors in (Mercat et al., 2018) proposed an ML method to determine the
optimal partitioning of a given CU and to predict whether a CU can be combined
with the CU of the previous depth or split into more CUs. The results of their
approach demonstrated that the features extracted from the depth and variance
information are the most relevant parameters for predicting the CU
decomposition using ML to achieve a balance between computational complexity
and accuracy. (Jillani et al., 2020) introduced an unsupervised multi-view
clustering technique to identify correlations between intra-prediction mode
decisions and information extracted from the texture data of adjacent CUs in
HEVC. Their approach achieved an average time reduction of around 50.4,
resulting in reduced energy consumption compared to the reference HM 16.8
encoder.

####  4.2.2. Inter-frame prediction

Inter-frame prediction relies on previously encoded reference frames to
estimate the movement of objects between frames using motion vectors and
compensation. These vectors indicate the displacement of each block between
consecutive frames (Sullivan et al., 2012). In (Zhang et al., 2015), the
authors proposed a close method (Zhang et al., 2017) using a joint SVM
classifier with three outputs to determine whether to split a CU. In uncertain
cases, full rate-distortion optimization determines the CU depth. This process
explores all combinations and selects those that reduce the computational
complexity by 51.45 compared to the original HEVC, consequently reducing the
energy consumption.

####  4.2.3. Motion compensation

Arnaudov et al., (Arnaudov and Ogunfunmi, 2020) proposed an ANN model for
motion estimation in HD video sequences, improving video quality and balancing
performance and power consumption. A dictionary of previously learned fixed
search patterns and a pre-trained ANN model can select the best search pattern
according to the motion dynamics within a specific region of the video frame.
The method achieves the highest motion estimation quality per unit of consumed
power, i.e., 0.5 less PSNR than a complete search method while minimizing the
search spacee.

###  4.3. Video transcoding

Video transcoding involves modifying an encoded video to convert it from one
coding standard to another. Another form of transcoding is adapting specific
parameters like QP, bitrate, or resolution without changing the codec, often
known as transrating. This process encompasses both the encoding and decoding
procedures, making it a computationally complex and power-intensive operation.
The state-of-the-art AI-enabled energy-aware transcoding algorithms in the
literature can be categorized based on the conversion of codec, bitrate, and
QP.

####  4.3.1. Codec

Yang et al., (Yang et al., 2022) proposed an ML-based fast transcoding method
from Distributed Video Coding (DVC) (Lu et al., 2019) to HEVC. Initial
features, such as motion information extracted from the DVC decoding, capture
relevant characteristics to predict the CU partition and PU modes in the HEVC
encoding using an SVM binary classification task, resulting in a noteworthy
57.64 reduction in encoding complexity and energy consumption.

####  4.3.2. Bitrate

Bubolz et al., (Bubolz et al., 2019) used a Decision Tree (DT) model with $15$
features from the decoding process, such as QP, CU position in X and Y
directions, and color information to predict CU depth for the HEVC standard.
This method achieved early termination in CU partitioning, leading to a 49.5
reduction in energy consumption, with only a 0.66 increase in Bjøntegaard
Delta (BD)-rate utilized to compare the compression efficiency of different
codecs, where positive values indicate that the new codec or configuration is
less efficient in terms of bitrate or quality (Barman et al., 2024).

####  4.3.3. QP

Costero et al., (Costero et al., 2019) employed an RL-based approach to manage
the runtime of QoS-sensitive real-time video transcoding using three agents,
with the primary agent tasked with adapting the QP of the HEVC encoder. The
authors strategically decomposed the design space, encompassing various
encoding parameters, system settings, and resource allocation choices
affecting QoS, power consumption, throughput, and video compression efficiency
during transcoding, enabling RL agents to concentrate on specific parameter
subsets or configurations. Compared to equivalent approaches, the method
demonstrated a notable 24 reduction in energy.

Table 3. Summary of important works in video encoding (4) (SI: Spatial
Information, TI: Temporal Information, CI: Color Information, BI: Brightness
Information, MI: Motion Information, BR: Bitrate, FR: Framerate, Res:
Resolution, RD: Rate-Distortion).

_Section_ | Work | Year | Scope | AI Approach | Evaluation Method
---|---|---|---|---|---
Input | Model | Output | Reference Software |  Dataset
4.1.1 | (Katsenou et al., 2021) | 2021 | QP selection | SI, TI | GP | QP | HM(HM, 2024) | (Afonso et al., 2019)
(Mueller et al., 2022) | 2022 | Bitrate ladder | Video size, | CNN, ANN, | Bitrate ladder | (Mueller et al., 2022) | Generated by authors
construction | BR, FR, Res | XGBoost,
(Amirpour et al., 2023) | 2023 | Preset selection | TI, BI | RF | Video preset | x265 (MulticoreWare, 2024) | (Amirpour et al., 2022)
(Menon et al., 2024a) | 2024 | Res selection | SI, TI, BR | RF | Encoding time, XPSNR | VVenc(VVenC, 2024) | (Stergiou and Poppe, 2022)
(Azimi Ourimi et al., 2024) | 2024 | Res and QP | SI, TI, FR, Res, | XGBoost | Energy and VMAF | x264 (x264, 2024), x265 (MulticoreWare, 2024), | (Amirpour et al., 2024)
selection | QP, Codec type | VVenc(VVenC, 2024), AV1 (Han et al., 2021)
4.1.2 | (Seeliger et al., 2022) | 2022 | Bitrate ladder | Video frame, Res, CI | CNN, ANN, | BR | Elemental (AWS, 2024), (Mueller et al., 2022) | Generated by authors
construction | XGBoost
4.2 | (Zhang et al., 2017) | 2017 | CU partitioning | CU information | SVM | CU size | HM(HM, 2024) | (Bossen, 2013)
(Mercat et al., 2018) | 2018 | CU information | ML | CU size | Kvazaar (Viitanen et al., 2015) | Generated by authors
(Jillani et al., 2020) | 2020 | Texture | ML | Intra-prediction modes | HM(HM, 2024) | Generated by authors
(Zhang et al., 2015) | 2015 | MI, RD and Depth | SVM | CU size | (Bossen, 2013)
of CU, QP
(Arnaudov and Ogunfunmi, 2020) | 2020 | Motion vector | MI | ANN | Searching pattern | (Xiph.org, 2024)
4.3 | (Yang et al., 2022) | 2022 |  | MI, Residual | SVM | CU size | Generated by authors
| variability information
(Bubolz et al., 2019) | 2019 | Transcoding | QP, CI, CU position | DT | CU size | (Sharman and Rosewarne, 2017)
(Costero et al., 2019) | 2019 | acceleration | BR, PSNR, | RL | QP | Kvazaar (Viitanen et al., 2015) | (Bossen, 2013)
| Throughput,
| Power consumption

###  4.4. Encoding summary

Table 3 summarizes the AI-based for encoding configuration, redundancy
reduction, transcoding improvement, and considering energy consumption.
XGBoost and RF are the most frequent models for regression tasks, such as
bitrate or encoding time prediction, while SVM models are predominantly used
for classification, particularly for CU partitioning. Common implementations
for HEVC include x265 (MulticoreWare, 2024), HM (HM, 2024), and Kvazaar
(Viitanen et al., 2015), while typical implementations for VVC are VTM (VTM,
2024) and VVenC (VVenC, 2024).

##  5\. Delivery Network

As mentioned above, the CMCD and CMSD specifications have recently garnered
attention for facilitating communication between clients and media servers.
CMCD provides a standardized method for the streaming client to communicate
metrics and playback data to the servers, such as buffer information, playback
rate, and content identification, ensuring that the CDN can consistently
interpret and analyze the data from various streaming clients. Hence, numerous
AI-based network-assisted video streaming systems leverage these protocols to
improve network QoS, user QoE, and energy efficiency. One suitable AI
technique for such systems is RL, which can be employed on CDN or MEC servers.
For example, RL agents illustrated in Fig. 7 leverage CMCD and CMSD
information to incorporate network, video, user, and CDN data in their
decision-making process for content cache placement (Li et al., 2019; Tang et
al., 2019) to achieve lower latency, higher QoE, and lower delivery energy,
creating a reward function communicated to the CDN or MEC servers through the
CMCD system. In the following, we categorize AI-based energy-efficient
network-assisted video streaming solutions according to their delivery
networks. As several studies have introduced novel techniques for edge
instances without distinguishing between MEC and CDN, we employ the edge
network term in our taxonomy.

###  5.1. Edge network

Enhancing video streaming workflows on edge networks, whether CDN or MEC,
requires pivotal choices regarding content caching, video processing task
offloading and scheduling, and resource allocation methods.

####  5.1.1. Content caching

Tanzil et al., (Tanzil et al., 2017) proposed a single hidden-layer feed-
forward neural network, named Extreme Learning Machine (ELM), to predict the
popularity of video content based on user behavior and network properties
(e.g., cache size, bandwidth). They used thumbnails, titles, keywords, and
descriptions of YouTube video sequences as inputs to the model and proposed a
Mixed Integer Linear Programming (MILP) optimization model (Genova and
Guliashki, 2011) to determine the cache with the minimum cache deployment
cost, i.e., the energy consumption required to read/write files from hardware
units. This method improved the cache hit ratio of $0.9$ compared to random
cache deployment, consequently improving deployment cost.

Figure 7. RL-based streaming system example.

Li et al., (Li et al., 2021) presented a Multi-Layer Perceptron (MLP) model to
predict user behavior based on the titles, categories, and descriptions of
video sequences. They used an optimization model (Quesada and Grossmann, 1992)
to determine the optimal caching strategy for popular video content on edge
servers driven by latency and energy consumption considerations, leading to an
energy reduction of 1800, a latency decrease of 120, and an 80 improvement in
cache hit rates compared to baseline approaches.

Zheng et al., (Zheng et al., 2021) proposed an LSTM model Moreover, thet
popular content, integrated into a Deep Deterministic Policy Gradient (DDPG)
(Tan, 2021) algorithm to learn an optimal caching policy in energy consumption
and latency in continuous VR action spaces based on user requests. The results
indicated that the method exceeds baseline random selection and a traditional
DDPG (without LSTM) to trade off energy and latency.

Xie (Xie, 2024) optimized energy-efficient video caching and delivery in
cellular networks using Deep Reinforcement Learning (DRL) agents leveraging
network conditions and user demands to decide caching resources and user
associations with base stations. They adjusted the model’s rewards, achieving
a 40 improvement in energy consumption, including the energy costs of video
transmission, compared to baseline random selection.

####  5.1.2. Offloading, scheduling, and allocation

Chen and Liu (Chen and Liu, 2021) introduced an energy-efficient DRL-based
model for video processing task offloading and resource allocation in AR
applications. The state of the DRL model encompasses the data size, computing
and transmission resources, and network conditions. The reward is the energy
consumed when task completion and delay constraints are met. They used two
methods for task offloading: binary and partial. In the binary-based method,
the decision is made whether to offload a task to the edge server or execute
it on the user’s device. In contrast, in partial task offloading, each task is
segmented into subtasks, with each subtask partially offloaded to edge
servers. These subtasks include receiving the raw video frame from the camera,
tracking the user’s position in the environment, identifying objects, and
preparing the processed frame for display. Their solution achieved an energy
consumption reduction of 75 compared to a greedy approach and 30 compared to a
random baseline method.

Lu et al., (Lu et al., 2020) introduced a QoE-aware video processing
offloading method utilizing a DRL model tailored for Internet of Things (IoT)
devices. The reward function of their approach incorporates service latency,
energy consumption, and task success rate, while the model states encompass
data size for each task, Transmission loss, and the computation resources
available on edge servers. The results indicated an improvement in energy
consumption and service latency by 13.97 and 11.56 compared to a DDPG
approach. Baker et al., in (Baker et al., 2024) utilized an LSTM model
considering energy consumption, latency, and memory usage across various
tasks, particularly on video processing tasks, to identify the most reliable
edge node for offloading video processing tasks such as real-time video
analysis. Subsequently, they introduced a DRL model to intelligently allocate
video processing tasks to the selected edge devices, aiming to expedite task
execution and improve latency and energy consumption. The results demonstrated
notable improvements in both energy efficiency and latency compared to
baseline methods (Yan and Choudhury, 2021; Huang et al., 2019). Zhu et al.,
(Zhu et al., 2022) introduced an RL-based model to implement an energy-
efficient edge resource allocation for video processing tasks. In this
solution, the agent states include edge capacity and video processing task
size, while the actions involve assigning edge instances to video processing
tasks, specifically for handling AI computations such as training and
inference. The reward function is also designed based on power consumption,
providing a comprehensive energy efficiency measure within the edge computing
environment. The results indicated that the energy consumption of the proposed
method is 30 lower than that of the first-in, first-out baseline allocation
strategy.

###  5.2. Peer-to-Peer (P2P)

AI-based solutions are commonly applied in P2P video delivery, specifically
for predicting user locations and content popularity and optimizing content
caching and delivery. Li et al., (Li et al., 2019) employed two distinct
recurrent neural network-based architectures, i.e., Echo State Network (ESN)
(Jaeger, 2007) and LSTM, for predicting user locations and content popularity,
respectively. The ESN model used historical location information, while the
LSTM incorporated user-related details such as gender, age, occupation, and
device type. They also introduced a DRL model to strike a balance between
energy consumption and delay and to determine the optimal device for
connection based on predicted data from neural networks. The proposed method
achieves lower energy consumption and reduced content delivery delay compared
to the baselines used in the research.

P2P content delivery providers employ several incentives to motivate peers to
share their network or computational resources to benefit the entire network.
These incentives can vary according to the policies of each P2P provider. For
example, the “tit-for-tat” principle can be offered, where active peers
receive faster download speeds when they contribute their upload bandwidth to
other peers. Another example is the “reward-based” policy, where active peers
receive compensation in terms of monetary rewards or free Internet services
for their resource contributions (He et al., 2022). Therefore, to enhance
video delivery and reduce server energy consumption, users can allocate a
portion of their available computing resources, in addition to network
bandwidth, to have distributed video processing (e.g., transcoding or super-
resolution) over the P2P network.

Liu et al., (Liu et al., 2018) introduced a DRL-based model to select optimal
peers for running transcoding functions. The reward function incorporates
factors such as other users’ trust, historical records of successful
transcoding ratios, the status of the peer battery, and its energy consumption
pattern. The results validated that their approach achieved superior
transcoding efficiency while meeting QoS requirements and outperformed the
baseline greedy method. Farahani et al., (Farahani et al., 2022a) employed a
Self Organizing Map (SOM) (Kohonen, 2012) to propose an online learning model
for handling live video requests in a hybrid P2P-CDN network. This model
effectively determines the serving action, whether from the P2P side through
video transcoding or caching or from the CDN side through edge caching or edge
transcoding, while taking serving latency into consideration. They extended
their approach in (Farahani et al., 2023b) by incorporating a video super-
resolution running at the client side called “peer super-resolution” and
considering delivery cost in their decision-making process. The results
revealed a 22 improvement in users’ QoE, a 34 reduction in streaming service
provider costs, and a 31 decrease in edge server energy consumption compared
to alternative greedy strategies.

Table 4. Summary of important works in delivery network (5) (RTT: Round Trip
Time, Bandwidth: BW).

_Section_ | Work | Year | Scope | AI Approach | Evaluation Method
---|---|---|---|---|---
Input | Model | Output | Real Testbed/Simulator |  Dataset
5.1.1 | (Tanzil et al., 2017) | 2017 | Cache placement | Thumbnails, Title, | ELM | Popular video | NS-3(Mastorakis et al., 2016) | Generated by authors
Keywords, Description
(Li et al., 2021) | 2021 | Video title, Category, | MLP | $10$ servers, $25$ clients | (Wolf et al., 2011a, b)
Streaming duration,
Description, No. requests
(Zheng et al., 2021) | 2021 | Cache capacity, | DRL | Cache location | Numerical simulation | Generated by authors
Video popularity
(Xie, 2024) | 2024 | Cache capacity, Network | Numerical simulation | Generated by authors
BW and congestion | ($10$ clients)
5.1.2 | (Chen and Liu, 2021) | 2021 | Video processing | Task size, | DRL | Offloading location | Numerical simulation | Generated by authors
task offloading and | Network BW, | ($3$ servers, $15$ clients,
resource allocation | Computing capacity |  $3$ base stations)
(Lu et al., 2020) | 2020 | Video processing | Task size, Transmission | Numerical simulation | Generated by authors
task offloading | loss, Computational capacity
(Baker et al., 2024) | 2024 | Video processing | Energy consumption, Execution | Python simulation | (Dos Anjos et al., 2021; Deng et al., 2014)
task | and communication time, | ($5$ edge devices, $10$ tasks)
offloading | BW availability, Trust scores |
(Zhu et al., 2022) | 2022 | Video processing | Computational capacity, | RL | Resource scheduling |  $200$ NVIDIA | Generated by authors
| resource allocation | Task size | Jeston devices (Nvidia, 2024)
5.2 | (Li et al., 2019) | 2019 | Cache placement | User’s information, | ESN, | Popular video, | Numerical simulation | Generated by authors
Age, Location | LSTM | Device location | ($200$ peers)
Device location, Popularity | DRL | Cache location |
(Liu et al., 2018) | 2018 | Peer selection | Workload, Energy consumption, | DRL | Transcoder selection | Numerical simulation | Generated by authors
for transcoding | QoS, peer’s trust score | ($10$ peers)
(Farahani et al., 2022a, 2023b) | 2022 | Request serving | Buffer status, BW, | SOM | Serving action | $5$ servers, $350$ peers | (Lederer et al., 2012; Zoo, 2024)
| by super-resolution | Bitrate,
| and transcoding | Computation power
5.3 | (Zhong et al., 2022) | 2022 | MPTCP delivery | Packet loss, RTT, | RL | Transmission path | NS-3(NS3, 2024) | Generated by authors
path selection | Energy consumption,
(Shams et al., 2023) | 2023 | Network selection | Network throughput, | MLP | Connection device | $10$ Raspberry Pis | Generated by iperf (Dugan et al., 2009)
for device connection | Energy consumption

###  5.3. Emerging delivery protocols

In recent years, various networking protocols, such as QUIC (IETF, 2012), Web
Real-Time Communication (WebRTC) (WebRTC, 2011), and Multipath Transmission
Control Protocol (MPTCP) (Ford et al., 2013), have been utilized in the
streaming domain (Mohamed and Mahmood, 2023; Zverev et al., 2021; Ran et al.,
2023; Han et al., 2024). These protocols contribute to reducing latency and
QoE. Our search methodology revealed a limited emphasis on leveraging AI
models to enhance energy efficiency and QoE within such protocols. For
instance, the authors in (Zhong et al., 2022) introduced an RL-based model for
MPTCP video delivery. The RL agent determines when and over which path the
video packets should be transmitted, optimizing the delivery of high-quality
5G media services. The state of the proposed RL model encompasses the packet
loss rate in each path, the energy consumption of data transferring in each
path, and the Round-Trip Time (RTT), while the reward function is composed of
throughput, packet loss, and energy consumption. This method outperformed the
lowest-RTT-first (Paasch et al., 2014) and earliest completion first (Lim et
al., 2017) baseline methods in terms of completion time, transmission rate,
and energy consumption. Shams et al., (Shams et al., 2023) proposed a Deep
Learning (DL) model to improve the energy consumption of MPTCP video delivery
using two algorithms: a Software-Defined Network (SDN)-based method
determining the network for device connection; an algorithm adjusting the
decision of the first algorithm based on energy consumption. They also
utilized an MLP model pre-trained on different network sizes and MPTCP
congestion control algorithms. This method reduced energy consumption by 19
and improved network performance by 8 compared to other baselines, such as
MPTCP Cubic (Rhee et al., 2018).

###  5.4. Delivery summary

Table 4 summarizes works that utilized AI techniques to present energy-aware
delivery services provided by single or hybrid edge-P2P networks as well as
the features of emerging delivery protocols. Our analysis shows that RL-based
methods, particularly DRL, are the most popular models for offloading,
scheduling, allocation, and caching problems. Moreover, the most common
evaluation approaches are simulating the end-to-end aspects of video streaming
by numerical simulations, Python-based frameworks, or NS-3.

##  6\. Video Playback

In recent years, AI techniques have enhanced ABR algorithms, incorporating
energy efficiency alongside traditional factors like QoE and network
throughput. In addition, client-side AI-based video quality enhancement
techniques, such as video super-resolution and contrast adjustments, are
typically used to optimize QoE during video playback.

###  6.1. ABR algorithms

Turkkan et al., (Turkkan et al., 2022) developed an RL-based ABR algorithm
called GreenABR, incorporating states of energy consumption, network
throughput, video player current buffer size, the bitrate of the most recently
downloaded segment, and the VMAF score. They used an ANN model to predict the
playback energy consumption with only 7 error based on input variables such as
bitrate, resolution, framerate, file size, and VMAF. The ANN-based energy
model, leveraging network throughput and video player measurements, optimizes
the selection of video representations, ensuring energy-efficient choices.
They designed the RL reward function based on quality, buffering, and energy
consumption factors. The results demonstrated a $57\text{\,}\mathrm{\char
37\relax}\text{/}$ reduction in power consumption compared to the buffer-based
BOLA ABR algorithm (Spiteri et al., 2020). The authors extended their research
to GreenABR+ (Turkkan et al., 2024), employing the same energy model and using
VMAF as the perceived quality metric. GreenABR+ tackles the challenge of
retraining models for diverse video representation sets by employing a DRL
model encompassing a broader bitrate range. GreenABR+ outperforms both
GreenABR (Turkkan et al., 2022) and Pensieve (Mao et al., 2017), demonstrating
a 57 reduction in energy consumption and an 87 improvement in rebuffering
time.

Mondal et al., (Mondal et al., 2020) introduced an energy-efficient DASH video
player (EnDASH) designed to balance user QoE and energy consumption for
devices in mobility scenarios. The player employed three consecutive
prediction models, resulting in a nearly $30\text{\,}\mathrm{\char
37\relax}\text{/}$ reduction in energy consumption compared to the state-of-
the-art Pensive (Mao et al., 2017):  (1) an RF model to predict the average
throughput of the cellular network using historical information and
characteristics such as associated cellular technology (i.e., 2G-4G) and the
number of neighboring base stations;  (2) an RL model to predict and determine
whether to increase or decrease the buffer length based on a reward function
considering energy savings and QoE score. The RL states include the average
cellular network throughputs predicted in the previous step, the current
playback buffer capacity, and a set of potential changes in the buffer length;
(3) a DRL model utilizes states, such as the predicted buffer length,
throughput, and the previous segment’s bitrate, to select the next segment
based on the QoE score.

Given the belief in the necessity for an ABR algorithm to adapt more
effectively to specific network conditions rather than performing well on
average across all conditions, Huang (Huang et al., 2022) proposed an ABR
algorithm, $A^{\scriptsize 2}BR$, consisting of online and offline stages. In
the offline stage, they trained an RL model with diverse scenarios of network
conditions. This trained model is then utilized by the video player in the
online stage to select the optimal bitrate based on the current network
conditions. The results confirmed a $7\text{\,}\mathrm{\char
37\relax}\text{/}$ improvement in VMAF while consuming the same amount of
energy compared to the baseline approach (Yin et al., 2015). Raman et al.,
(Raman et al., 2024) introduced a DRL method for energy-efficient live video
streaming on mobile devices called LL-GABR. LL-GABR states encompassed player
metrics such as current buffer size, latency, rebuffering time, playback rate,
bitrate, VMAF, network predicted bandwidth and player energy consumption. They
designed a reward function based on high video quality, minimized rebuffering,
reduced bitrate switches, and enhanced energy efficiency. LL-GABR showed
performance improvements compared to other adaptive bitrate algorithms, such
as (Gutterman et al., 2020; Sun et al., 2021), achieving a 44 improvement in
QoE and a 73 enhancement in energy efficiency.

###  6.2. Video super-resolution

Super-resolution refers to a computational task to upscale and restore high-
frequency signals that may be lost in a low-resolution image or video,
ultimately generating a high-resolution counterpart. This process aims to
minimize visual degradation and enhance image quality as much as possible,
resulting in a high-resolution image or video (Wang et al., 2020). Many DL-
based super-resolution models such as (Dong et al., 2016; Ledig et al., 2017;
Ahn et al., 2018; Shi et al., 2016; Liu et al., 2021) have emerged in recent
years that stand out for their innovative architectures and impressive
performance.

Given that video super-resolution often runs on end-user devices with limited
resources and batteries, significant research efforts have been directed
towards leveraging AI to design resource and energy-aware super-resolution
techniques. Menon et al., (Menon et al., 2024c) introduced a method to
determine the encoding resolution by maximizing the achievable VMAF using
FSRCNN (Dong et al., 2016). They employed an RF model to predict the VMAF
scores of the video after applying a super-resolution model and the encoding
time for each determined resolution. Their results showed a 32.70 reduction in
bitrate while maintaining the same VMAF compared to the HLS bitrate ladder,
leading to a 68.21 reduction in encoding energy consumption. The authors in
(Yue et al., 2022) proposed a lightweight CNN model by enhancing the
efficiency of a well-performing model, ABPN (Du et al., 2021), by pruning
residual connections. Residual connections, commonly used in neural networks
such as ABPN, facilitate the flow of information by providing shortcut paths
for gradients during training. The pruning process indeed reduces
computational overhead by selectively removing redundant connections within
residual blocks without reducing the model’s performance. The optimized model
achieved a power consumption of $0.7\text{\,}\mathrm{W}\text{/}$, an inference
time of $2.03\text{\,}\mathrm{ms}\text{/}$, and a PSNR of
$27.46\text{\,}\mathrm{dB}\text{/}$. The results showcased lower power
consumption and 43 improvement in inference time compared to ABPN while
preserving nearly identical PSNR levels.

Gao et al., (Gao et al., 2022) introduced a re-parameterization technique to
enrich the network’s feature extraction capabilities with multiple paths
during training. Subsequently, they expedited network inference by
consolidating multiple paths into a single convolution block. They achieved a
PSNR of $27.52$ with a power consumption of $0.1\text{\,}\mathrm{W}\text{/}$
on the identical dataset used in (Yue et al., 2022). Zhang et al., (Zhang et
al., 2023b) proposed an efficient method for running Deformable Convolutional
Networks (DCNs) on Field Programmable Gate Arrays (FPGAs) for video super-
resolution. In their approach, the authors introduced optimizations at two
levels: algorithm and hardware. In algorithm-level optimization, they used a
lightweight network to reduce complexity and memory usage, while at the
hardware level, they designed a dedicated processing core and storage to
handle irregular memory access caused by deformable convolutions. The
experiments demonstrated a $1.63$ times enhancement in energy efficiency
compared to other FPGA-based super-resolution baselines.

The authors in (Farahani et al., 2023b) proposed a framework for live video
streaming (ALIVE), considering all available computational resources from
peers, MEC servers, and CDNs. They leveraged video super-resolution for DASH
requests in the P2P layer of their architecture, upscaling low-resolution
frames received from adjacent peers. Experimenting with various CNN-based
super-resolution techniques, they selected CARN (Ahn et al., 2018) for PC and
LiDeR (Çetinkaya et al., 2022) for mobile peers. The framework achieved a
$22\text{\,}\mathrm{\char 37\relax}\text{/}$ improvement in QoE and a
$31\text{\,}\mathrm{\char 37\relax}\text{/}$ reduction in edge energy
consumption. Inspired by the concept of super-resolution to construct high-
resolution videos from low-resolution counterparts, the authors in (Moghaddam
et al., 2024) employed a CNN-based model to mitigate the visual distortions
inherent in video encoding, adeptly and effectively reconstructing the video
sequence from low-bitrate video sequences, yielding a 40 improvement in
perceived quality, as measured by both PSNR and SSIM metrics Compared to
baseline methods.

###  6.3. Video contrast adjustment

The user device’s display, whether a smartphone, laptop, TV, or PC,
constitutes another component contributing to power consumption in the video
streaming lifecycle. The display’s power consumption typically depends on its
technology, such as LCD, OLED, or AMOLED (Kim et al., 2013). Video contrast on
displays involves the differentiation in brightness or color between each
frame’s brightest and darkest element, impacting visibility and detail
sharpness. Proper contrast alignment optimizes display technology utilization
and minimizes unnecessary power consumption while improving visibility without
compromising energy efficiency in the display process.

Shin et al., (Shin et al., 2019) presented a CNN model tailored for OLED
displays, aiming to boost video frame contrast and decrease power consumption.
Their methodology selectively reduced brightness by a specified ratio while
preserving perceived quality through contrast enhancement using a conditional
generative adversarial network. This network generated output frames under the
constraint of power consumption, resulting in a significant power saving rate
of up to $42.1\text{\,}\mathrm{\char 37\relax}\text{/}$. Meur et al., (Le Meur
et al., 2023) focused on reducing display energy consumption using a pixel-
wise content-adaptive method to generate an attenuation map on the encoder
side. They used a CNN model, providing information on adjusting pixel values
for energy reduction. The results indicated that at an energy reduction rate
of 10, the average VMAF score for different video sequences demonstrated a 6
improvement compared to a linear scaling method.

Table 5. Summary of important works in video playback (6) (BR: Bitrate, BS:
Base Station, RTT: Round Trip Time, SR: Super-resolution, TR: Transcoding CA:
Contrast Adjustment).

_Section_ | Work | Year | Scope | AI Approach | Evaluation Method
---|---|---|---|---|---
Input | Model | Output | Real Device/Simulator |  Dataset
6.1 | (Mondal et al., 2020) | 2020 |  | Cellular technology ($2$G-$4$G), | RF | Throughput | Moto G5, | Generated by
Developing DASH | No. BSs, Download rate | Micromax Canvas | authors
video players | Network throughput, Buffer | RL | Bitrate | Infinity |
| size, Buffer length |  |
(Turkkan et al., 2022), (Turkkan et al., 2024) | 2022 |  | Energy consumption, BR, VMAF, | RL - DRL | Bitrate | Samsung S4 | (Duanmu et al., 2018)
| Network throughput, Buffer size
(Huang et al., 2022) | 2022 |  | VMAF, Network throughput, | RL | Bitrate | MacBook Pro | (Yan et al., 2020),(Narayanan et al., 2020), (Pensieve, 2017)
Developing | Download time, Buffer occupancy
(Bentaleb et al., 2023) | 2023 | ABR | Network throughput, RTT, | RL | Bitrate | (Mao et al., 2019) | (Van Der Hooft et al., 2016; Riiser et al., 2013; Narayanan et al., 2021; Duanmu et al., 2020)
algorithms | Playback rate, Packet loss,
| Buffer occupancy, Resolution
(Raman et al., 2024) | 2024 |  | Buffer size, VMAF, Bitrate, Energy | DRL | Bitrate, Playback rate | Python simulation | (Xiph.org, 2024)
| consumption, Rebuffering time
6.2 | (Gao et al., 2022) | 2022 |  | Video frame | CNN | Upscaled frame | Python simulation | (Nah et al., 2019)
(Yue et al., 2022) | 2022 |  | CNN | Redmi K50 Pro | (Nah et al., 2019)
(Zhang et al., 2023b) | 2023 | Developing | DCN | Python simulation |  (Xue et al., 2019)
(Moghaddam et al., 2024) | 2024 | SR methods | CNN | Higher quality | Python, NVIDIA GPUs | Generated by authors
| (PSNR, SSIM)
(Menon et al., 2024c) | 2024 |  | Spatial, temporal information | RF | Resolution | Python simulation | (Amirpour et al., 2022)
(Farahani et al., 2023b) | 2023 | SR and TR on battery- | Video frame | CNN | Upscaled frame | iPhone 11, Xiaomi | (Lederer et al., 2012; Zoo, 2024)
constrained end devices | Mi11, virtual machines(Ricci et al., 2014)
6.3 | (Shin et al., 2019) | 2019 | CA methods | Higher contrast | Python simulation | (Arbelaez et al., 2010)
(Le Meur et al., 2023) | 2023 | Pixel-value | OLED display | (Bossen et al., 2019; Arbelaez et al., 2010)
modification map

###  6.4. Playback Summary

Table 6 summarizes AI-based ABR algorithms that incorporate content-aware
information to enhance bitrate selection and energy efficiency, primarily
utilizing (D)RL models and evaluated on smartphones. Our investigation also
highlights client-based AI technologies, i.e., super-resolution and video
contrast adjustment, which aim to balance quality enhancement with energy
efficiency. These works mainly used CNN-based models and employed a
combination of real devices and simulations for evaluation.

##  7\. Video Quality Assessment (VQA)

Expanding on the taxonomy of VQA metrics introduced in Section 3, these
metrics can be further categorized as Full Reference (FR), Reduced Reference
(RR), and No Reference (NR) methods, depending on the degree of information
available for the reference video (Lin and Kuo, 2011). VMAF, PSNR, and SSIM
are in the FR-VQA category since they evaluate the quality of compressed video
by directly comparing it to the original version. In contrast, NR-VQA methods
do not utilize the original video content for quality assessment. Therefore,
AI techniques are frequently employed to analyze distorted videos
independently without relying on a reference video. Moreover, RR-VQA methods
offer the advantage of evaluating video quality using limited information,
resulting in lower power consumption compared to FR-VQA methods, making them
suitable for energy-efficient video streaming applications (Dost et al.,
2022).

###  7.1. Reduced Reference (RR) and No Reference (NR) VQA prediction

VMAF is a popular choice for quality evaluation due to its stronger
correlation with perceptual quality compared to SSIM and PSNR. However, its
computational intensity and high time complexity pose challenges (Madhusudana
et al., 2021). Menon et al., (Menon et al., 2024b) introduced an LSTM-based
RR-VQA approach to predict VMAF scores for reconstructed videos. Their
proposed method incorporated features such as Discrete Cosine Transform
(DCT)-based texture information and a fusion of SSIM between the original and
reconstructed videos. The results demonstrated a $9.14$ times acceleration
compared to traditional VMAF estimation, resulting in an
$89.44\text{\,}\mathrm{\char 37\relax}\text{/}$ reduction in energy
consumption. Barman et al., in (Barman et al., 2019) used four learning
models, GP, MLP, Support Vector Regression (SVR), and RF to propose an NR-VQA
method to predict VMAF scores in video gaming applications. The models
incorporated input such as encoding parameters, content, and compression
artifacts besides Blind Image Quality Index (BIQI) (Moorthy and Bovik, 2010)
and Natural Image Quality Evaluator (NIQE) (Mittal et al., 2012) as two well-
known NR image quality methods adapted for video sequences. The results
revealed that the SVR model exhibited the best performance compared to ground-
truth VMAF.

###  7.2. QoE prediction

Many works consider bitrate a primary factor in QoE assessment. However, the
use of HTTPS by OTT providers to secure data transmission complicates the
packet inspection and information extraction (e.g., bitrate) from encrypted
network traffic (Duanmu et al., 2020; Li et al., 2023). Pan and Cheng (Pan and
Cheng, 2018) employed three ML models: RF for handling diverse data patterns,
Bayesian Networks for probabilistic reasoning, and AdaBoost for enhancing weak
classifiers, to evaluate QoE and energy efficiency in encrypted YouTube
streaming. They specifically trained these models to estimate video bitrates
from HTTPS traffic, resulting in energy savings of up to 20.

Ickin et al., (Ickin et al., 2021) introduced a Federated Learning (FL)
technique for QoE estimation, specifically focusing on energy efficiency and
lightweight training. Their approach involves partitioning the dataset across
various local devices, each holding a subset of features and a central model
trained by communicating with these distributed datasets. Comparative analysis
against a fully centralized model showcased the efficacy of their proposed
method, demonstrating comparable performance. The authors delved into
hyperparameter tuning, focusing on energy efficiency and light training,
resulting in a noteworthy 28 reduction in training time and a subsequent
decrease in energy consumption associated with model training. Kougioumtzidis
et al., (Kougioumtzidis et al., 2024) addressed the influence of the wireless
transmission channel on user QoE and network QoS parameters, such as
bandwidth, jitter, latency, and packet loss. They used a CNN-based model to
predict the QoE of video games in wireless networks, promoting efficient
utilization of computational resources and reducing network energy
consumption. This method achieved an MAE value of $0.09$, outperforming
baselines with MAE metrics ranging from $0.1$ to $0.7$.

Table 6. Summary of important works in video quality assessment (7) (BR:
Bitrate, SI: Spatial Information, TI: Temporal Information, BI: Brightness
Information, FR: Framerate).

_Section_ | Work | Year | Scope | AI Approach | Evaluation Method
---|---|---|---|---|---
Input | Model | Output | Device/Reference Software/Simulator |  Dataset
7.1 | (Menon et al., 2024b) | 2024 | VMAF prediction | SI, TI, BI | LSTM | VMAF | x264(x264, 2024) | (Amirpour et al., 2022)
(Barman et al., 2019) | 2019 | BR, Res, SI, TI, Blur, | SVR | VMAF | MATLAB simulation | (Barman et al., 2019, 2018)
Blockiness, BIQI, NIQE
7.2 | (Pan and Cheng, 2018) | 2018 | MOS prediction | BR, Segment size, | RF, AdaBoost, | Bitrate | iPhone 5, 6, 6s, HTC M7, | Generated by authors
Res, Segment duration | Bayes Network | Samsung S4, Xiaomi MI2
(Ickin et al., 2021) | 2021 | SI, TI, FR, BR, No. stalls, | Vertical FL | MOS | Python simulation | (Duanmu et al., 2018)
Stall duration, MOS
(Kougioumtzidis et al., 2024) | 2024 | Bandwidth, Jitter, | CNN | MOS | OpenAirInterface (Kaltenberger et al., 2020) | Generated by authors
| Network latency, Packet loss

###  7.3. VQA summary

Table 6 summarizes the work that utilized AI techniques to reduce the
dependency on reference frames by the NR and RR-VQA methods. Moreover, it
highlights work that predicts QoE information, such as bitrate, within HTTPS
traffic, aiming to balance QoE assessment and energy efficiency. The
investigation reveals no single dominant ML model or evaluation method for the
discussed contributions. The choice of model and evaluation approach varies
based on specific sub-problems.

##  8\. Future Research Directions

This section outlines potential future research directions for AI-driven,
energy-efficient video streaming systems, focusing on the segments of the
video streaming lifecycle and the role of generative AI in enhancing the
performance of these segments.

###  8.1. Video encoding

Beyond integrating AI into conventional codecs like HEVC and VVC, there is
growing interest in end-to-end learning-based coding schemes (Zhang et al.,
2017; Jillani et al., 2020; Mercat et al., 2018). Unlike traditional methods
that optimize encoding and decoding separately, end-to-end approaches use
integrated DL models to optimize the entire video compression process as a
unified learning task. In this context, extensive datasets, including pairs of
original video frames and their compressed representations, are typically used
to train networks, enabling them to proficiently map input video frames to
their compressed counterparts (Jia et al., 2023). In particular, the MPAI-EEV
standard, developed by the Moving Picture, Audio, and Data Coding by
Artificial Intelligence (MPAI) group, demonstrates superior perceptual
evaluation metrics compared to the VVC codec (Jia et al., 2023). For example,
in (Ma et al., 2020), a DL-based end-to-end compression scheme achieved a 23
improvement in BD-rate on average compared to HEVC. However, further research
is needed to explore the impact of this approach on energy consumption and
carbon emissions similar to the studies like (Katsenou et al., 2022; Kränzler
et al., 2020; Herglotz et al., 2017) conducted on existing codecs.

###  8.2. Delivery network

The increasing prevalence of edge computing, softwarized and virtualized
networks, and technologies like _EdgeAI_ is driving the integration of AI into
critical network and resource management tasks. These tasks include optimizing
resource allocation, scheduling video processing, and enhancing user request
servicing through intelligent server and segment selection strategies. Given
the resource-intensive nature of AI models, it is imperative to optimize both
accuracy and energy consumption (Xu et al., 2024; Ji et al., 2023). A
promising strategy involves decentralizing computations, encompassing both
training and inference, by leveraging edge devices. This approach offers
significant advantages, particularly in reducing computation time and energy
consumption (Zhang et al., 2023a). Leveraging emerging computing technologies,
such as serverless computing (Denninnart and Amini Salehi, 2024; Patros et
al., 2021), with AI to enhance the sustainability, efficiency, and monetary
cost of video streaming systems presents new opportunities. In addition, using
intelligent in-network components to assist video encoders in reducing
bitrates and energy consumption through constructing dynamic bitrate ladders
while maintaining high quality and user experience, or enhancing network
resource utilization in multi-CDN environments (Viola et al., 2020), e.g.,
bandwidth and server capacity, are promising yet underexplored strategies.

###  8.3. Video playback

Recent research indicates that encouraging streaming users to adopt
environmentally conscious preferences, such as accepting certain quality
degradations to reduce CO2 emissions, can significantly impact the energy
consumption of video streaming (Hossfeld et al., 2023; Bingöl et al., 2023).
Therefore, the next improvement for CMCD and CMSD protocols or AI-based ABRs
may involve incorporating energy-awareness information, enabling both video
players and delivery components to provide green services. In addition,
optimizing AI techniques for lower energy consumption on the client side,
particularly for computationally intensive tasks such as video super-
resolution, remains an open research topic to ensure efficiency and
sustainability.

###  8.4. VQA

AI-based VQA solutions are often perceived as black boxes, posing challenges
in understanding the relationship between input factors and model outcomes
(Wehner et al., 2023). However, adopting Explainable AI (XAI) techniques
(Clement et al., 2024; Wehner et al., 2023)can mitigate this challenge by
providing clear explanations of the model’s behavior and the rationale behind
specific decisions. This is particularly crucial for energy-efficient video
streaming, where transparent AI decisions can optimize energy consumption
without compromising user experience.

###  8.5. Generative AI

The emergence of Generative AI and Large Language Models (LLMs) (Hoffmann et
al., 2022; Chowdhery et al., 2023; Touvron et al., 2023) is revolutionizing
the video streaming lifecycle, similar to their impact on other applications
(Zhou et al., 2024). Current integrations have reduced the complexity of
conventional compression techniques (Mentzer et al., 2022), enabled players to
adjust data transmission rates through more precise network bandwidth
predictions (Azmin et al., 2022), accelerated super-resolution models (Mentzer
et al., 2022), and developed ABR algorithms customized for diverse network
conditions (Zhou et al., 2024). However, the models behind Generative AIs and
LLMs are massive and energy-intensive. However, the models behind Generative
AIs and LLMs with numerous features are massive and energy-intensive. Recent
research revealed that training a single LLM can emit up to $284$ tons of CO2,
equivalent to the lifetime emissions of five cars, while inference processes
also demand significant computational resources (Strubell et al., 2019; Bingöl
et al., 2023). Consequently, a significant research gap exists concerning the
energy efficiency of these methods for streaming use cases.

##  9\. Conclusion

The increase in video consumption across various devices has raised concerns
about the CO2 emissions attributed to video streaming, which require solutions
to mitigate the environmental impact. This survey investigated AI-based
solutions to reduce energy consumption in primary aspects of the video
streaming lifecycle, i.e., video encoding, delivery, and playback. It provided
essential background on encoding, delivery networks, and playback as integral
components of the video streaming lifecycle. It explored VQA and energy models
and metrics. It also presented a taxonomy and a state-of-the-art review of
$59$ AI-based energy-aware solutions in four sections. Finally, it outlined
potential future research directions, highlighting opportunities to improve
energy efficiency in video streaming using AI methods.

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