PACC: Perception Aware Congestion Control for Real-time Communication

被引:2
|
作者
Peng, Feng [1 ]
Lu, Bingcong [1 ]
Song, Li [1 ,3 ]
Xie, Rong [1 ]
Liu, Yanmei [2 ]
Chen, Ying [2 ]
机构
[1] Shanghai Jiao Tong Univ, Sch Elect Informat & Elect Engn, Shanghai, Peoples R China
[2] Alibaba Grp, Hangzhou, Peoples R China
[3] Shanghai Jiao Tong, MoE Key Lab Artificial Intelligence, AI Inst, Shanghai, Peoples R China
关键词
real-time communication; congestion control; perception; quality of experience; QUALITY;
D O I
10.1109/ICME55011.2023.00172
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Due to the network fluctuations, congestion control is indispensable to guarantee the quality of experience (QoE) for Real-Time Communication (RTC) users. This component adjusts the sending rate of media data, which determines the video encoding bitrate. However, existing control schemes either only focus on network numerical indicators or fail to adapt to various network environments. Logically, we propose PACC (Perception Aware Congestion Control) for RTC in this paper. Leveraging the convolutional neural network (CNN), we develop a quality sensor to infer the video quality increasing rate. Assisted with the variation trend analysis for user perception, PACC tunes the bitrate towards the direction of better QoE. Extensive trace-driven experiments demonstrate the effectiveness of PACC, which outperforms the existing landmark schemes by 8.2% to 32.4% and 6.8% to 18.0% in terms of transport and application layer QoE metrics, respectively.
引用
收藏
页码:978 / 983
页数:6
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