Decentralized Collaborative Video Caching in 5G Small-Cell Base Station Cellular Networks

TL;DR

This paper introduces a distributed collaborative caching algorithm for 5G small-cell base stations that efficiently reduces video delivery delay and bandwidth usage, even with unknown video popularities, through a near-optimal, scalable solution tested on real traces.

Contribution

The paper presents a novel distributed caching algorithm for 5G small-cell networks that is scalable, near-optimal, and adaptable to online popularity estimation, improving video delivery performance.

Findings

Algorithm reduces video delivery delay in simulations.

Effective bandwidth conservation on remote servers.

Outperforms existing caching methods in real trace tests.

Abstract

We consider the problem of video caching across a set of 5G small-cell base stations (SBS) connected to each other over a high-capacity short-delay back-haul link, and linked to a remote server over a long-delay connection. Even though the problem of minimizing the overall video delivery delay is NP-hard, the Collaborative Caching Algorithm (CCA) that we present can efficiently compute a solution close to the optimal, where the degree of sub-optimality depends on the worst case video-to-cache size ratio. The algorithm is naturally amenable to distributed implementation that requires zero explicit coordination between the SBSs, and runs in $O(N + K \log K)$ time, where $N$ is the number of SBSs (caches) and $K$ the maximum number of videos. We extend CCA to an online setting where the video popularities are not known a priori but are estimated over time through a limited amount of periodic information sharing between SBSs. We demonstrate that our algorithm closely approaches the optimal integral caching solution as the cache size increases. Moreover, via simulations carried out on real video access traces, we show that our algorithm effectively uses the SBS caches to reduce the video delivery delay and conserve the remote server's bandwidth, and that it outperforms two other reference caching methods adapted to our system setting.