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

Shi，Wanxin1,2; Li，Qing2; Wang，Chao3; Zou，Longhao2,4; Shen，Gengbiao1,5; Zhang，Pei6; Jiang，Yong1,2

10.1145/3485447.3511944

2022-04-25

31st ACM Web Conference (WWW)

WWW 2022 - Proceedings of the ACM Web Conference 2022

3289-3297

APR 25-29, 2022

null,null,ELECTR NETWORK

1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES

ASSOC COMPUTING MACHINERY

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.

DASH Edge Computing Reinforcement Learning

其他

英语

CPCI-S ; EI

Key-Area Research and Development Program of Guangdong Province[2020B0101130006] ; National Natural Science Foundation of China[61972189] ; Shenzhen Key Lab of Software Defined Networking[ZDSYS20140509172959989] ; Social Science Fund of Shenzhen[SZ2020D009] ; China Postdoctoral Science Foundation[2021M700091]

Computer Science

Computer Science, Cybernetics ; Computer Science, Software Engineering ; Computer Science, Theory & Methods

WOS:000852713003036

20222012110658

Image communication systems ; Quality of service ; Reinforcement learning ; Video streaming

Digital Computers and Systems:722.4 ; Artificial Intelligence:723.4

2-s2.0-85129893988

Scopus

1.International Graduate School,Tsinghua University,Shenzhen,China
2.Peng Cheng Laboratory,Shenzhen,China
3.Tsinghua-Berkeley Shenzhen Institute,Tsinghua University,Shenzhen,China
4.Southern University of Science and Technology,Shenzhen,China
5.Sangfor Technologies Incorporation,Shenzhen,China
6.Beijing University of Posts and Telecommunications,Beijing,China

Shi，Wanxin,Li，Qing,Wang，Chao,et al. Learning-based Fuzzy Bitrate Matching at the Edge for Adaptive Video Streaming[C]. 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES:ASSOC COMPUTING MACHINERY,2022:3289-3297.