Updating Content in Cache-Aided Coded Multicast

Abstract

Motivated by applications to delivery of dynamically updated, but correlated data in settings such as content distribution networks, and distributed file sharing systems, we study a single source multiple destination network coded multicast problem in a cache-aided network. We focus on models where the caches are primarily located near the destinations, and where the source has no cache. The source observes a sequence of correlated frames, and is expected to do frame-by-frame encoding with no access to prior frames. We present a novel scheme that shows how the caches can be advantageously used to decrease the overall cost of multicast, even though the source encodes without access to past data. Our cache design and update scheme works with any choice of network code designed for a corresponding cache-less network, is largely decentralized, and works for an arbitrary network. We study a convex relation of the optimization problem that results form the overall cost function. The results of the optimization problem determines the rate allocation and caching strategies. Numerous simulation results are presented to substantiate the theory developed.

The Role of Caching in Future Communication Systems and Networks

“…The paper [24] extends coded caching to wireless broadcast channels with Gaussian noise, and studies the optimization of energy efficiency. Reference [25] addresses the problem of combining network coding and caching in order to optimize a multicast session over a directed-acyclic graph. In [26] the authors study the fundamental limits of secretive coded caching, examining the case of partially known prefetched files.…”

Codes for Updating Linear Functions over Small Fields

“…In this section we first derive an upper bound on the optimal codelength l q,opt over F q and then provide code constructions for (A, ) function update problem when A is in form given by (4). Define η = max z∈I FU wt(z).…”

“…Hence choosing S as a parity check matrix of an [m, m − 2 ] MDS code F q , q ≥ m and encoder matrix H = SA S we achieve codelength l q,opt = 2 which matches the codelength achieved by the construction given in Section IV of [1] which also requires q ≥ m. Now from [12] we have k 2 (4, 3) = 1. Hence choosing S as parity check matrix of a [4,1] repetition code over F 2 we achieve codelength l 2,opt = 3.…”

“…Comparison of Code Construction 1 and Code Construction 2 for (A S , 1) function update problem with t ≥ 1: In this sub-section we consider the Code Construction 2 for the special case of = 1 and then compare the performance with the performance of the Code Construction 1. Consider an (A S , 1) function update problem where A S is of the form given in (4). To obtain a valid code for the (A S , 1) function update problem using the Code Construction 2, we use a linear code over F q t of maximum possible dimension with blocklength a and minimum distance d min ≥ 3.…”

“…The authors in [1] also consider a broadcast scenario where a codeword is broadcast to multiple nodes in order to update the (different) linear functions stored in each of the nodes. Problems related to updating linear functions have been considered in [4]- [6]. In [4], codes for updating linear functions are used in cache-aided networks to reduce the cost of multicasting a sequence of correlated data frames.…”