深度强化学习在边缘视频传输优化中的应用综述  

Survey on Applications of Deep Reinforcement Learning in Edge Video Transmission Optimization

作　　者：李彦[1,2] 万征 LI Yan;WAN Zheng(School of Information Engineering,Nanchang Institute of Technology,Nanchang 330099,China;School of Information Management and Mathematics,Jiangxi University of Finance and Economics,Nanchang 330032,China)

机构地区：[1]南昌工程学院信息工程学院,南昌330099 [2]江西财经大学信息管理与数学学院,南昌330032

出　　处：《计算机工程与应用》2025年第4期43-58,共16页Computer Engineering and Applications

基　　金：国家自然科学基金(61961021);江西省教育厅科技计划重点项目(GJJ180251)。

摘　　要：在产业视频时代下,边缘计算、人工智能技术的迅猛发展,催生了边缘智能,基于边缘计算网络的视频传输优化研究迎来了新机遇。在总结边缘视频传输优化内容的基础上,梳理深度强化学习等人工智能技术应用于边缘视频传输优化的研究与进展情况;提出边缘智能视频传输优化概念,给出面向网络QoS的边缘智能视频传输优化和面向用户QoE的边缘智能视频传输优化的方法分类,并分别进行详细阐述;研究分析目前边缘视频传输优化仍然存在的主要问题,通过寻找规律,分析不足,突出优势,指出边缘智能视频传输优化未来的热点研究方向。In the era of industrial video,with the rapid development of edge computing and artificial intelligence technology,the edge intelligence has been born,and the research of video transmission optimization based on edge computing network has ushered in new opportunities.Based on the summary of the content of edge video transmission optimization,this paper expounds the research and progress of deep reinforcement learning applied to edge video transmission optimization.The concept of edge intelligent video transmission optimization is proposed,and the method classification of edge intelligent video transmission optimization is presented.It consists of edge intelligent video transmission optimization for network QoS and edge intelligent video transmission optimization for user QoE,and the detailed description is respectively given.The main problems of edge video transmission optimization at present are studied and analyzed,the future hot research directions of edge intelligent video transmission optimization are pointed out by finding laws,summarizing deficiencies and highlighting advantages.

关 键 词：移动边缘计算 深度强化学习 边缘智能 边缘智能视频传输优化 

分 类 号：TN919.8[电子电信—通信与信息系统] TP391[电子电信—信息与通信工程]