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GreenABR: energy-aware adaptive bitrate streaming with deep reinforcement learning

Authors:

Bekir Oguzhan Turkkan,

Muhammed Fatih Bulut,

Daby SowAuthors Info & Claims

MMSys '22: Proceedings of the 13th ACM Multimedia Systems Conference

Pages 150 - 163

https://doi.org/10.1145/3524273.3528188

Published: 05 August 2022 Publication History

Abstract

Adaptive bitrate (ABR) algorithms aim to make optimal bitrate decisions in dynamically changing network conditions to ensure a high quality of experience (QoE) for the users during video streaming. However, most of the existing ABRs share the limitations of predefined rules and incorrect assumptions about streaming parameters. They also come short to consider the perceived quality in their QoE model, target higher bitrates regardless, and ignore the corresponding energy consumption. This joint approach results in additional energy consumption and becomes a burden, especially for mobile device users. This paper proposes GreenABR, a new deep reinforcement learning-based ABR scheme that optimizes the energy consumption during video streaming without sacrificing the user QoE. GreenABR employs a standard perceived quality metric, VMAF, and real power measurements collected through a streaming application. GreenABR's deep reinforcement learning model makes no assumptions about the streaming environment and learns how to adapt to the dynamically changing conditions in a wide range of real network scenarios. GreenABR outperforms the existing state-of-the-art ABR algorithms by saving up to 57% in streaming energy consumption and 60% in data consumption while achieving up to 22% more perceptual QoE due to up to 84% less rebuffering time and near-zero capacity violations.

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Index Terms

GreenABR: energy-aware adaptive bitrate streaming with deep reinforcement learning

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Published In

cover image ACM Conferences

MMSys '22: Proceedings of the 13th ACM Multimedia Systems Conference

June 2022

432 pages

ISBN:9781450392839

DOI:10.1145/3524273

General Chairs:
Niall Murray
Technological University of the Shannon: Midlands Midwest
,
Gwendal Simon
Synamedia
,
Mylene Farias
University of Brasilia
,
Program Chairs:
Irene Viola
Centrum Wiskunde & Informatica
,
Mario Montagud
i2CAT Foundation & University of Valencia

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 05 August 2022

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MMSys '22: 13th ACM Multimedia Systems Conference

June 14 - 17, 2022

Athlone, Ireland

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Cited By

Seufert MSpangenberger MPoignée FWamser FRobitza WTimmerer CHoßfeld T(2024)COBIRAS: Offering a Continuous Bit Rate Slide to Maximize DASH Streaming Bandwidth UtilizationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/367737920:10(1-24)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3677379
Park CShende CSen SWang B(2024)C2: ABR Streaming in Cognizant of Consumption Context for Improved QoE and Resource Usage TradeoffsACM Transactions on Multimedia Computing, Communications, and Applications10.1145/365251720:9(1-27)Online publication date: 16-Aug-2024
https://dl.acm.org/doi/10.1145/3652517
Turkkan BDai TRaman AKosar TChen CBulut MZola JSow D(2024)GreenABR+: Generalized Energy-Aware Adaptive Bitrate StreamingACM Transactions on Multimedia Computing, Communications, and Applications10.1145/364989820:9(1-24)Online publication date: 5-Mar-2024
https://dl.acm.org/doi/10.1145/3649898
Xiang WYuan YSu YYang CTang D(2024)Energy optimized mobile video streaming with user behaviorThird International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)10.1117/12.3031377(331)Online publication date: 19-Jul-2024
https://doi.org/10.1117/12.3031377
Ye JQin SXiao QJiang WTang XLi X(2024)Adaptive Bitrate Algorithms via Deep Reinforcement Learning With Digital Twins Assisted TrajectoryIEEE Transactions on Network Science and Engineering10.1109/TNSE.2024.337645111:4(3522-3535)Online publication date: Jul-2024
https://doi.org/10.1109/TNSE.2024.3376451
Bentaleb ALim MAkcay MBegen AZimmermann R(2024)Bitrate Adaptation and Guidance With Meta Reinforcement LearningIEEE Transactions on Mobile Computing10.1109/TMC.2024.337656023:11(10378-10392)Online publication date: Nov-2024
https://doi.org/10.1109/TMC.2024.3376560
Wang YLi JLi ZShang SLiu Y(2024)Synergistic Temporal-Spatial User-Aware Viewport Prediction for Optimal Adaptive 360-Degree Video StreamingIEEE Transactions on Broadcasting10.1109/TBC.2024.337411970:2(453-467)Online publication date: Jun-2024
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Azimi ZFarahani RMenon VTimmerer CProdan R(2024)Towards ML-Driven Video Encoding Parameter Selection for Quality and Energy Optimization2024 16th International Conference on Quality of Multimedia Experience (QoMEX)10.1109/QoMEX61742.2024.10598278(80-83)Online publication date: 18-Jun-2024
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Zhang GGuo J(2024)E-Stream: An Energy-Efficient Approach to Short-form Video Streaming in Mobile Devices2024 IEEE Symposium on Computers and Communications (ISCC)10.1109/ISCC61673.2024.10733696(1-6)Online publication date: 26-Jun-2024
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Kalan RDulger I(2024)A Survey on QoE Management Schemes for HTTP Adaptive Video Streaming: Challenges, Solutions, and OpportunitiesIEEE Access10.1109/ACCESS.2024.349161312(170803-170839)Online publication date: 2024
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