Correlation-Aware Distributed Caching and Coded Delivery

TL;DR

This paper introduces a correlation-aware approach to distributed caching and coded delivery, utilizing joint file compression to improve load reduction in cache-aided multicast systems by exploiting content correlations.

Contribution

It generalizes cache-aided coded multicast to include content correlation, proposing a joint compression scheme that outperforms existing correlation-unaware methods.

Findings

Joint compression reduces load beyond existing schemes

Correlation-aware scheme approaches optimal rate-memory trade-off

Significant performance gains over state-of-the-art solutions

Abstract

Cache-aided coded multicast leverages side information at wireless edge caches to efficiently serve multiple groupcast demands via common multicast transmissions, leading to load reductions that are proportional to the aggregate cache size. However, the increasingly unpredictable and personalized nature of the content that users consume challenges the efficiency of existing caching-based solutions in which only exact content reuse is explored. This paper generalizes the cache-aided coded multicast problem to a source compression with distributed side information problem that specifically accounts for the correlation among the content files. It is shown how joint file compression during the caching and delivery phases can provide load reductions that go beyond those achieved with existing schemes. This is accomplished through a lower bound on the fundamental rate-memory trade-off as well as a correlation-aware achievable scheme, shown to significantly outperform state-of-the-art correlation-unaware solutions, while approaching the limiting rate-memory trade-off.