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.

翻译：大多数基于学习的码率自适应算法局限于离线学习，这不可避免地存在仿真与现实之间的差距。在线学习能更好地适应动态实时通信场景，但仍面临训练收敛时间过长的挑战。本文提出一种名为Bamboo的新型在线分组联邦迁移学习框架，旨在加速训练效率。初步实验证明，该算法在多种网络条件下，相比其他强化学习算法可将在线训练效率提升高达302%，同时保障实时视频通信的体验质量（QoE）。