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.
@article{arxiv.2308.09948,
title = {Bamboo: Boosting Training Efficiency for Real-Time Video Streaming via Online Grouped Federated Transfer Learning},
author = {Qianyuan Zheng and Hao Chen and Zhan Ma},
journal= {arXiv preprint arXiv:2308.09948},
year = {2023}
}