Real-time Bandwidth Estimation from Offline Expert Demonstrations
Aashish Gottipati Sami Khairy Gabriel Mittag Vishak Gopal Ross Cutler
In this work, we tackle the problem of bandwidth estimation (BWE) for
real-time communication systems; however, in contrast to previous works, we
leverage the vast efforts of prior heuristic-based BWE methods and synergize
these approaches with deep learning-based techniques. Our work addresses
challenges in generalizing to unseen network dynamics and extracting rich
representations from prior experience, two key challenges in integrating
data-driven bandwidth estimators into real-time systems. To that end, we
propose Merlin, the first purely offline, data-driven solution to BWE that
harnesses prior heuristic-based methods to extract an expert BWE policy.
Through a series of experiments, we demonstrate that Merlin surpasses
state-of-the-art heuristic-based and deep learning-based bandwidth estimators
in terms of objective quality of experience metrics while generalizing beyond
the offline world to in-the-wild network deployments where Merlin achieves a
42.85% and 12.8% reduction in packet loss and delay, respectively, when
compared against WebRTC in inter-continental videoconferencing calls. We hope
that Merlin's offline-oriented design fosters new strategies for real-time
network control.