Coded caching with nonuniform demands

By Urs Niesen, Mohammad Ali Maddah-Ali. Conference on Computer Communications Workshops, 2013

389 citations recorded by the source corpus.

We consider a network consisting of a file server connected through a shared link to a number of users, each equipped with a cache. Knowing the popularity distribution of the files in the database, the goal is to optimally populate the caches such as to minimize the expected load of the shared link. For a single user, it is well known that caching the most popular files is optimal in this setting. However we show here that this is no longer the case for multiple users. Indeed, caching only the most popular files can be highly suboptimal. Instead, a fundamentally different approach is needed, in which the cache contents are used as side information for coded communication over the shared link. We propose such a coded caching scheme and prove that it is close to optimal.

Subfields: Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/63958a6d9ea16f217eaf6820a25cabc65d766d87

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In Computer Networks and Communications

Citations in the corpus

Fundamental Limits of Caching in Wireless D2D Networks (2014, methodology): “For each u ∈ U , the function φ u : The delivery phase is deﬁned by two sets of functions: the node encoding functions, denoted by { ψ u : u ∈ U} , and the node decoding functions, denoted by { λ u : u ∈ U} .”
Decentralized coded caching attains order-optimal memory-rate tradeoff (2013, background): “It is also a key ingredient to extending the coded-caching approach to scenarios with nonuniform demands or with online cache updates, as is discussed further in Section VI and in the follow-up works [9] and [10].”
Online Coded Caching (2013, background): “The demand for video streaming services such as those offered by Netﬂix, YouTube, Amazon, and others, is growing rapidly.”
Fundamental Limits of Caching (2012, background): “Furthermore, the results in [18] hold only when all users have the same file popularity distribution.”
The Exact Rate-Memory Tradeoff for Caching With Uncoded Prefetching (2016, methodology): “The caching problem has also been extended in various directions, including decentralized caching [17], online caching [18], caching with nonuniform demands [19]–[22], hierarchical caching [23]–[25], device-to-device caching [26], cache-aided interference channels [27]–[30], caching on file selectio”