Anableps: Adapting Bitrate for Real-Time Communication Using VBR-encoded Video
Zicheng Zhang, Hao Chen, Xun Cao, Zhan Ma

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsImage and Video Quality Assessment · Video Coding and Compression Technologies · Multimedia Communication and Technology
