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

TL;DR

QARC employs deep reinforcement learning to adaptively select bitrates in real-time video streaming, balancing perceptual quality and transmission latency under varying network conditions.

Contribution

It introduces a novel DRL-based rate control algorithm that predicts future perceptual quality and optimizes bitrate selection for improved streaming performance.

Findings

QARC outperforms existing rate control methods in emulation tests.

The neural network accurately predicts perceptual video quality.

QARC achieves higher perceptual quality with lower latency.

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.