  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.
