  Most of the learning-based algorithms for bitrate adaptation are limited to
offline learning, which inevitably suffers from the simulation-to-reality gap.
Online learning can better adapt to dynamic real-time communication scenes but
still face the challenge of lengthy training convergence time. In this paper,
we propose a novel online grouped federated transfer learning framework named
Bamboo to accelerate training efficiency. The preliminary experiments validate
that our method remarkably improves online training efficiency by up to 302%
compared to other reinforcement learning algorithms in various network
conditions while ensuring the quality of experience (QoE) of real-time video
communication.
