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arXiv · 2308.09948

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

Qianyuan Zheng·Hao Chen·Zhan Ma
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Abstract

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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Qianyuan Zheng, Hao Chen, Zhan Ma. 2023-08-19. Bamboo: Boosting Training Efficiency for Real-Time Video Streaming via Online Grouped Federated Transfer Learning. https://doi.org/10.1145/3600061.3600069

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