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Content providers increasingly replace traditional constant bitrate withvariable bitrate (VBR) encoding in real-time video communication systems forbetter video quality. However, VBR encoding often leads to large and frequentbitrate fluctuation, inevitably deteriorating the efficiency of existingadaptive bitrate (ABR) methods. To tackle it, we propose the Anableps toconsider the network dynamics and VBR-encoding-induced video bitratefluctuations jointly for deploying the best ABR policy. With this aim, Anablepsuses sender-side information from the past to predict the video bitrate rangeof upcoming frames. Such bitrate range is then combined with the receiver-sideobservations to set the proper bitrate target for video encoding using areinforcement-learning-based ABR model. As revealed by extensive experiments ona real-world trace-driven testbed, our Anableps outperforms the GCC withsignificant improvement of quality of experience, e.g., 1.88x video quality,57% less bitrate consumption, 85% less stalling, and 74% shorter interactiondelay.

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