On Coding for Cache-Aided Delivery of Dynamic Correlated Content

TL;DR

This paper introduces a correlation-aware caching and delivery scheme that leverages content correlations to improve load reduction in dynamic, personalized media services, outperforming existing correlation-unaware methods.

Contribution

It generalizes cache-aided multicast to account for content correlation, designing schemes that utilize this correlation for more efficient delivery in dynamic content scenarios.

Findings

Correlation-aware schemes outperform state-of-the-art solutions.

Leveraging content correlation reduces network load significantly.

Effective for real-time and personalized media services.

Abstract

Cache-aided coded multicast leverages side information at wireless edge caches to efficiently serve multiple unicast demands via common multicast transmissions, leading to load reductions that are proportional to the aggregate cache size. However, the increasingly dynamic, 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 specifically account for the correlation among content files, such as, for example, the one between updated versions of dynamic data. It is shown that (i) caching content pieces based on their correlation with the rest of the library, and (ii) jointly compressing requested files using cached information as references during delivery, can provide load reductions that go beyond those achieved with existing schemes. This is accomplished via the design of a class of correlation-aware achievable schemes, shown to significantly outperform state-of-the-art correlation-unaware solutions. Our results show that as we move towards real-time and/or personalized media dominated services, where exact cache hits are almost non-existent but updates can exhibit high levels of correlation, network cached information can still be useful as references for network compression.