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QAVA: QoE-Aware Adaptive Video Bitrate Aggregation for HTTP Live Streaming Based on Smart Edge Computing.

Authors :
Ma, Xiaoteng
Li, Qing
Zou, Longhao
Peng, Junkun
Zhou, Jianer
Chai, Jimeng
Jiang, Yong
Muntean, Gabriel-Miro
Source :
IEEE Transactions on Broadcasting. Sep2022, Vol. 68 Issue 3, p661-676. 16p.
Publication Year :
2022

Abstract

Currently video streaming in heterogeneous network environments is affected by limited network bandwidth availability and consequent low and variable user Quality of Experience (QoE) levels. In particular, for the case of live video streaming, a very high number of end-clients request content at the same time, generating huge concurrent traffic, and putting pressure on the existing network infrastructure. An approach which helps address this issue is deployment of emerging edge computing technologies to smooth the live streaming traffic and improve QoE by adapting client bitrates and caching content at the edge server. In this context, this paper proposes a novel QoE-aware Adaptive Video bitrate Aggregation scheme for HTTP live streaming based on smart edge computing (QAVA). As an intelligent proxy server, a “smart edge” which deploys QAVA aggregates all the traffic requested by clients for the same live streaming service and adapts their bitrates based on network conditions, client states and video characteristics. The adaptation is performed based on a Deep Reinforcement Learning (DRL)-based algorithm, which is also proposed. The QAVA DRL algorithm is trained and modeled based on a real client experience dataset. The experimental evaluation results presented in this paper show how QAVA outperforms other state-of-the-art adaptive bitrate algorithms in terms of average QoE and QoE fairness. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00189316
Volume :
68
Issue :
3
Database :
Academic Search Index
Journal :
IEEE Transactions on Broadcasting
Publication Type :
Academic Journal
Accession number :
158914483
Full Text :
https://doi.org/10.1109/TBC.2022.3171131