计算机应用研究2024，Vol.41Issue(4)：1150-1158,9.DOI:10.19734/j.issn.1001-3695.2023.08.0371
基于联邦共识机制的多视频流带宽分配策略
Multi-video stream bandwidth allocation strategy based on federated consensus mechanism
摘要
Abstract
This paper proposed a distributed video streaming fair scheduling strategy based on federated deep reinforcement learning to address the issues of unfair user QoE and low bandwidth utilization caused by uneven video bandwidth allocation in bottleneck links.This strategy dynamically generated bandwidth allocation weights based on the client's network status and the QoE level of each video stream.The congestion control algorithm at the server side allocated bandwidth to each video stream in the bottleneck link according to the computed weights,ensuring equitable transmission of video streams in the bottleneck link.Each video terminal operated a bandwidth allocation agent,and multiple agents train periodically using federated learning to fa-cilitate rapid convergence of the agent models.The bandwidth allocation agents synchronized their training parameters through a consensus mechanism,enabling distributed aggregation of the agent model parameters while ensuring the security of parameter sharing.Experimental results demonstrate that the proposed strategy improves QoE fairness and overall QoE efficiency by 10％and 7％,respectively,compared to the latest solutions.This indicates that the proposed strategy has potential and effectiveness in addressing the uneven allocation of video stream bandwidth and improving user experience.关键词
QoE公平性/视频质量/深度强化学习/联邦学习/区块链Key words
QoE fairness/video quality/deep reinforcement learning/federated learning/blockchain分类
信息技术与安全科学引用本文复制引用
张春阳,杨志刚,刘亚志,李伟..基于联邦共识机制的多视频流带宽分配策略[J].计算机应用研究,2024,41(4):1150-1158,9.基金项目
河北省高等学校科学技术研究资助项目(ZD2022102) （ZD2022102）
