Neil Agarwal and Rui Pan, Princeton University; Francis Y. Yan, University of Illinois Urbana-Champaign; Ravi Netravali, Princeton University
Rate control algorithms are at the heart of video conferencing platforms, determining target bitrates that match dynamic network characteristics for high quality. Despite the promise that recent data-driven strategies have shown for this challenging task, the performance degradation that they introduce during training has been a nonstarter for many production services, precluding adoption. This paper aims to bolster the practicality of data-driven rate control by presenting an alternate avenue for experiential learning: using purely existing telemetry logs that we surprisingly observe embed performant decisions but often at the wrong times or in the wrong order. To realize this approach despite the inherent uncertainty that log-based learning brings (i.e., lack of feedback for new decisions), our system, Mowgli, combines a variety of robust learning techniques (i.e., conservatively reasoning about alternate behavior to minimize risk and using a richer model formulation to account for environmental noise). Across diverse networks (emulated and real-world), Mowgli outperforms the widely deployed GCC algorithm, increasing average video bitrates by 15–39% while reducing freeze rates by 60–100%.
