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arXiv · 1805.02482

QARC: Video Quality Aware Rate Control for Real-Time Video Streaming via Deep Reinforcement Learning

Tianchi Huang·Rui-Xiao Zhang·Chao Zhou·Lifeng Sun
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Abstract

Due to the fluctuation of throughput under various network conditions, how to choose a proper bitrate adaptively for real-time video streaming has become an upcoming and interesting issue. Recent work focuses on providing high video bitrates instead of video qualities. Nevertheless, we notice that there exists a trade-off between sending bitrate and video quality, which motivates us to focus on how to get a balance between them. In this paper, we propose QARC (video Quality Awareness Rate Control), a rate control algorithm that aims to have a higher perceptual video quality with possibly lower sending rate and transmission latency. Starting from scratch, QARC uses deep reinforcement learning(DRL) algorithm to train a neural network to select future bitrates based on previously observed network status and past video frames, and we design a neural network to predict future perceptual video quality as a vector for taking the place of the raw picture in the DRL's inputs. We evaluate QARC over a trace-driven emulation. As excepted, QARC betters existing approaches.

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Tianchi Huang, Rui-Xiao Zhang, Chao Zhou, Lifeng Sun. 2018-05-07. QARC: Video Quality Aware Rate Control for Real-Time Video Streaming via Deep Reinforcement Learning. https://doi.org/10.1145/3240508.3240545

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