Title: Anableps: Adapting Bitrate for Real-Time Communication Using VBR-encoded Video
Abstract: Content providers increasingly replace traditional constant bitrate with variable bitrate (VBR) encoding in real-time video communication systems for better quality. However, VBR encoding often leads to significant and frequent bitrate fluctuation, inevitably deteriorating the efficiency of existing adaptive bitrate (ABR) methods. To tackle it, we propose that Anableps consider the network dynamics and VBR-encoding-induced video bitrate fluctuations jointly to deploy 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 de facto GCC with significant improvement in quality of experience, e.g., 1.88x video quality, 57% less bitrate consumption, 85% less stalling, and 74% shorter interaction delay.