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Improving Adaptive Real-Time Video Communication Via Cross-layer Optimization by Li, Yueheng; Chen, Hao; Xu, Bowei; Zhang, Zicheng; Ma, Zhan is a scholarly article available to read on EtoBox.

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Effective Adaptive BitRate (ABR) algorithm or policy is of paramount importance for Real-Time Video Communication (RTVC) amid this pandemic to pursue uncompromised quality of experience (QoE). Existing ABR methods mainly separate the network bandwidth estimation and video encoder control, and fine-tune video bitrate towards estimated bandwidth, assuming the maximization of bandwidth utilization yields the optimal QoE. However, the QoE of a RTVC system is jointly determined by the quality of compressed video, fluency of video playback, and interaction delay. Solely maximizing the bandwidth utilization without comprehensively considering compound impacts incurred by both network and video application layers, does not assure the satisfactory QoE. And the decoupling of network and video layer further exacerbates the user experience due to network-codec incoordination. This work therefore proposes the Palette, a reinforcement learning based ABR scheme that unifies the processing of network and video application layers to directly maximize the QoE formulated as the weighted function of video quality, stalling rate and delay. To this aim, a cross-layer optimization is proposed to derive f

Author

Li, Yueheng; Chen, Hao; Xu, Bowei; Zhang, Zicheng; Ma, Zhan

Published

2023

Language

EN