A Novel Distributed Media Caching Technique for Seamless Video Streaming in Multi-Access Edge Computing Networks

Abstract

Online video is anticipated to be the largest fraction of all mobile network traffic aside from the huge processing tasks imposed on networks by the billions of IoT devices, causing unprecedented challenges to the current network architecture. Edge caching has been proposed as a highly promising technology to overcome this challenge by placing computational and data storage resources at the network edge to reduce latency and backhaul traffic. However, the edge resources are heavily constrained in their storage and computational capacities as large-scale deployments mean fairly distributing resources across the network. Addressing this limitation, we propose an edge video caching scheme that dynamically caches the first part of popularity-ranked video files on Multi-Edge Computing Access Node (MAN) servers envisioned to achieve higher cache hit ratios, lower latencies, and lower backhaul traffic. The concept of Regionally Organized Clouds (ROCs) with sufficient resources for file caching and compute-intensive tasks was introduced, and a formulation of the edge caching problem as an Integer Linear Programming (ILP) problem was made. Additionally, this study proposes a file view-time threshold for each cached video aimed at reducing the resource wastage caused when buffered contents are abandoned. Comparative evaluations of the proposed show its excellent performance over FIFO, Greedy, LFRU and TLRU schemes.

Dynamic Computation Offloading with Deep Reinforcement Learning in Edge Networks

“…With these edge servers, data storage and processing occur outside the data generation device, reducing storage and data processing overhead. Thus, the EC paradigm efficiently increases the network capacity [5] and reduces the energy consumption of IoT devices. Moreover, geographically, due to the edge server's proximity to the users, the EC paradigm significantly improves the Quality of S ervice (QoS ).…”