Optimal decentralized coded caching for heterogeneous files

Han Cheng, Chenglin Li, Hongkai Xiong, Pascal Frossard

Abstract: Caching is a technique to reduce the peak network load by pre-fetching some popular contents at the local caches of end users. Coded caching can facilitate and exploit the coded-multicasting opportunities for users with different demands, resulting in an additional and significant reduction of the peak traffic. However, most existing researches on coded caching are limited by the assumption that all files to be delivered have the same size. We show in this paper that current schemes can only achieve suboptimal performance when the files have different sizes. To address this, we propose a novel optimization strategy for coded caching that minimizes the worst-case transmission rate of multicasting the coded content upon users requests, subject to the storage constraint at the local caches, by the optimal allocation of the caching proportion among heterogeneous files. In order to efficiently solve this problem, we develop a practical algorithm by using the Lagrange multiplier method and the sequential quadratic programming (SQP) approach. Experiment results show that the worst-case transmission rate can be reduced by the proposed scheme compared to state-of-the-art coded caching schemes. It certainly offers an important advantage in the deployment of data delivery systems.