Video on Demand Streaming Using RL-based Edge Caching in 5G Networks

Autor:	Nikbakht, Rasoul, Kahvazadeh, Sarang, Mangues-Bafalluy, Josep
Rok vydání:	2022
Předmět:	Computer Science - Networking and Internet Architecture
Druh dokumentu:	Working Paper
Popis:	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. Comment: 3 pages, 1 figure One page version of this paper has been accepted to 2022 IEEE Conference on Standards for Communications and Networking (CSCN) - Demo submissions
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2211.13962 Zobrazit plný text záznamu View this record from Arxiv