Bamboo: Boosting Training Efficiency for Real-Time Video Streaming via Online Grouped Federated Transfer Learning
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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.
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