基于联邦共识机制的多视频流带宽分配策略.

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 facilitate 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. [ABSTRACT FROM AUTHOR]

针对瓶颈链路中视频带宽分配不均导致的用户QoE不公平以及带宽利用率低的问题，提出了一种基于联邦深度强化学习的分布式视频流公平调度策略。该策略能够根据客户端网络状态和视频QoE等级动态生成带宽分配权重因子，服务器端的拥塞控制算法则根据带宽分配权重因子为瓶颈链路中的每个视频流分配带宽，以保障瓶颈链路中视频流的公平传输。每个视频终端都运行一个带宽分配agent，且多个agent以联邦学习的方式周期性地训练，以便代理模型能够快速收敛。带宽分配agent通过共识机制同步联邦训练参数，实现了在异步播放请求条件下带宽分配agent模型参数的分布式聚合，并确保了agent模型参数的安全共享。实验结果表明，与最新方案相比，提出策略在QoE公平性和整体QoE效率方面分别提高了10%和7%，这表明提出策略在解决视频流带宽分配不均问题和提升用户体验方面具有潜力和有效性。 [ABSTRACT FROM AUTHOR]