论文标题
使用基于RL的边缘缓存在5G网络中使用的视频按需流式传输
Video on Demand Streaming Using RL-based Edge Caching in 5G Networks
论文作者
论文摘要
边缘缓存可以在延迟和回程流量方面显着提高5G网络的性能。我们使用基于增强学习的（基于RL的）缓存技术，该技术可以适应按需视频内容的时间安排依赖性流行度模式。在私有5G中，我们将提出的缓存方案实现为两个虚拟网络功能（VNF），边缘和远程服务器，并将缓存命中率视为KPI。与HLS协议相结合，提出的视频按需（VOD）流是可靠的可靠服务，可以适应内容流行。
Edge caching can significantly improve the 5G networks' performance both in terms of delay and backhaul traffic. We use a reinforcement learning-based (RL-based) caching technique that can adapt to time-location-dependent popularity patterns for on-demand video contents. In a private 5G, we implement the proposed caching scheme as two virtual network functions (VNFs), edge and remote servers, and measure the cache hit ratio as a KPI. Combined with the HLS protocol, the proposed video-on-demand (VoD) streaming is a reliable and scalable service that can adapt to content popularity.
