Centralized Coded Caching of Correlated Contents

TL;DR

This paper introduces correlation-aware coded caching schemes that leverage content correlations to significantly reduce delivery rates compared to traditional methods that ignore such correlations.

Contribution

It proposes novel correlation-aware caching schemes and optimizes cache allocation based on content commonness levels, outperforming existing correlation-ignorant solutions.

Findings

Correlation-aware schemes outperform traditional methods.

Optimized cache allocation reduces delivery rate.

Exploiting content correlations benefits network efficiency.

Abstract

Coded caching and delivery is studied taking into account the correlations among the contents in the library. Correlations are modeled as common parts shared by multiple contents; that is, each file in the database is composed of a group of subfiles, where each subfile is shared by a different subset of files. The number of files that include a certain subfile is defined as the level of commonness of this subfile. First, a correlation-aware uncoded caching scheme is proposed, and it is shown that the optimal placement for this scheme gives priority to the subfiles with the highest levels of commonness. Then a correlation-aware coded caching scheme is presented, and the cache capacity allocated to subfiles with different levels of commonness is optimized in order to minimize the delivery rate. The proposed correlation-aware coded caching scheme is shown to remarkably outperform state-of-the-art correlation-ignorant solutions, indicating the benefits of exploiting content correlations in coded caching and delivery in networks.