  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.
