Anableps: Adapting Bitrate for Real-Time Communication Using VBR-encoded Video

TL;DR

Anableps introduces a reinforcement-learning-based adaptive bitrate method that jointly considers network dynamics and VBR-induced video bitrate fluctuations, significantly improving quality and efficiency in real-time video communication.

Contribution

It proposes a novel ABR approach that predicts video bitrate ranges using sender-side history and combines it with receiver observations for optimized encoding.

Findings

1.88x improvement in video quality

57% reduction in bitrate consumption

85% less stalling

Abstract

Content providers increasingly replace traditional constant bitrate with variable bitrate (VBR) encoding in real-time video communication systems for better video quality. However, VBR encoding often leads to large and frequent bitrate fluctuation, inevitably deteriorating the efficiency of existing adaptive bitrate (ABR) methods. To tackle it, we propose the Anableps to consider the network dynamics and VBR-encoding-induced video bitrate fluctuations jointly for deploying the best ABR policy. With this aim, Anableps uses sender-side information from the past to predict the video bitrate range of upcoming frames. Such bitrate range is then combined with the receiver-side observations to set the proper bitrate target for video encoding using a reinforcement-learning-based ABR model. As revealed by extensive experiments on a real-world trace-driven testbed, our Anableps outperforms the GCC with significant improvement of quality of experience, e.g., 1.88x video quality, 57% less bitrate consumption, 85% less stalling, and 74% shorter interaction delay.