Kim Minsu, Chung Kwangsue
Department of Electronics and Communications Engineering, Kwangwoon University, Seoul 01897, Korea.
Sensors (Basel). 2022 Mar 10;22(6):2171. doi: 10.3390/s22062171.
Dynamic Adaptive Streaming over HTTP (DASH) is a promising scheme for improving the Quality of Experience (QoE) of users in video streaming. However, the existing schemes do not perform coordination among clients and depend on fixed heuristics. In this paper, we propose an adaptive streaming scheme with reinforcement learning in edge computing environments. The proposed scheme improves the overall QoE of clients and QoE fairness among clients based on a state-of-the-art reinforcement learning algorithm. Edge computing assistance plays a role in providing client-side observations to the mobile edge, making agents utilize this information when generating a policy for multi-client adaptive streaming. We evaluated the proposed scheme through simulation-based experiments under various network conditions. The experimental results show that the proposed scheme achieves better performance than the existing schemes.
基于HTTP的动态自适应流（DASH）是一种有望提升视频流用户体验质量（QoE）的方案。然而，现有方案未在客户端之间进行协调，而是依赖固定的启发式方法。在本文中，我们提出了一种在边缘计算环境中采用强化学习的自适应流方案。所提方案基于一种先进的强化学习算法，提高了客户端的整体QoE以及客户端之间的QoE公平性。边缘计算辅助在向移动边缘提供客户端侧观测方面发挥作用，使智能体在为多客户端自适应流生成策略时能够利用此信息。我们通过在各种网络条件下基于仿真的实验对所提方案进行了评估。实验结果表明，所提方案比现有方案具有更好的性能。
