research-article

Learning-based Fuzzy Bitrate Matching at the Edge for Adaptive Video Streaming

Authors:

Yong JiangAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

Pages 3289 - 3297

https://doi.org/10.1145/3485447.3511944

Published: 25 April 2022 Publication History

Abstract

The rapid growth of video traffic imposes significant challenges on content delivery over the Internet. Meanwhile, edge computing is developed to accelerate video transmission as well as release the traffic load of origin servers. Although some related techniques (e.g., transcoding and prefetching) are proposed to improve edge services, they cannot fully utilize cached videos. Therefore, we propose a Learning-based Fuzzy Bitrate Matching scheme (LFBM) at the edge for adaptive video streaming, which utilizes the capacity of network and edge servers. In accordance with user requests, cache states and network conditions, LFBM utilizes reinforcement learning to make a decision, either fetching the video of the exact bitrate from the origin server or responding with a different representation from the edge server. In the simulation, compared with the baseline, LFBM improves cache hit ratio by 128%. Besides, compared with the scheme without fuzzy bitrate matching, it improves Quality of Experience (QoE) by 45%. Moreover, the real-network experiments further demonstrate the effectiveness of LFBM. It increases the hit ratio by 84% compared with the baseline and improves the QoE by 51% compared with the scheme without fuzzy bitrate matching.

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Cited By

He QTan SChen FXu XQi LHei XJin HYang YChen HDuh WHuang HKato MMothe JPoblete B(2023)EDIndex: Enabling Fast Data Queries in Edge Storage SystemsProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591676(675-685)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591676
Lin JWang S(2023)Improving robustness of learning-based adaptive video streaming in wildly fluctuating networks2023 IEEE International Conference on Multimedia and Expo (ICME)10.1109/ICME55011.2023.00307(1787-1792)Online publication date: Jul-2023
https://doi.org/10.1109/ICME55011.2023.00307

Index Terms

Learning-based Fuzzy Bitrate Matching at the Edge for Adaptive Video Streaming

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

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Publication History

Published: 25 April 2022

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Research-article
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Funding Sources

National Natural Science Foundation of China
Shenzhen Key Lab of Software Defined Networking
China Postdoctoral Science Foundation
Key-Area Research and Development Program of Guangdong Province
Social Science Fund of Shenzhen

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WWW '22

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SIGWEB

WWW '22: The ACM Web Conference 2022

April 25 - 29, 2022

Virtual Event, Lyon, France

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Cited By

He QTan SChen FXu XQi LHei XJin HYang YChen HDuh WHuang HKato MMothe JPoblete B(2023)EDIndex: Enabling Fast Data Queries in Edge Storage SystemsProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591676(675-685)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591676
Lin JWang S(2023)Improving robustness of learning-based adaptive video streaming in wildly fluctuating networks2023 IEEE International Conference on Multimedia and Expo (ICME)10.1109/ICME55011.2023.00307(1787-1792)Online publication date: Jul-2023
https://doi.org/10.1109/ICME55011.2023.00307

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