Adaptive Delivery in Caching Networks

The problem of content delivery in caching networks is investigated for scenarios where multiple users request identical files. Redundant user demands are likely when the file popularity distribution is highly non-uniform or the user demands are positively correlated. An adaptive method is proposed for the delivery of redundant demands in caching networks. Based on the redundancy pattern in the current demand vector, the proposed method decides between the transmission of uncoded messages or the coded messages of [1] for delivery. Moreover, a lower bound on the delivery rate of redundant requests is derived based on a cutset bound argument. The performance of the adaptive method is investigated through numerical examples of the delivery rate of several specific demand vectors as well as the average delivery rate of a caching network with correlated requests. The adaptive method is shown to considerably reduce the gap between the non-adaptive delivery rate and the lower bound. In some specific cases, using the adaptive method, this gap shrinks by almost 50% for the average rate.Comment: 8 pages,8 figures. Submitted to IEEE transaction on Communications in 2015. A short version of this article was published as an IEEE Communications Letter with DOI: 10.1109/LCOMM.2016.255814

Distortion-Memory Tradeoffs in Cache-Aided Wireless Video Delivery

Mobile network operators are considering caching as one of the strategies to keep up with the increasing demand for high-definition wireless video streaming. By prefetching popular content into memory at wireless access points or end user devices, requests can be served locally, relieving strain on expensive backhaul. In addition, using network coding allows the simultaneous serving of distinct cache misses via common coded multicast transmissions, resulting in significantly larger load reductions compared to those achieved with conventional delivery schemes. However, prior work does not exploit the properties of video and simply treats content as fixed-size files that users would like to fully download. Our work is motivated by the fact that video can be coded in a scalable fashion and that the decoded video quality depends on the number of layers a user is able to receive. Using a Gaussian source model, caching and coded delivery methods are designed to minimize the squared error distortion at end user devices. Our work is general enough to consider heterogeneous cache sizes and video popularity distributions.Comment: To appear in Allerton 2015 Proceedings of the 53rd annual Allerton conference on Communication, control, and computin

Effect of Number of Users in Multi-level Coded Caching

It has been recently established that joint design of content delivery and storage (coded caching) can significantly improve performance over conventional caching. This has also been extended to the case when content has non-uniform popularity through several models. In this paper we focus on a multi-level popularity model, where content is divided into levels based on popularity. We consider two extreme cases of user distribution across caches for the multi-level popularity model: a single user per cache (single-user setup) versus a large number of users per cache (multi-user setup). When the capacity approximation is universal (independent of number of popularity levels as well as number of users, files and caches), we demonstrate a dichotomy in the order-optimal strategies for these two extreme cases. In the multi-user case, sharing memory among the levels is order-optimal, whereas for the single-user case clustering popularity levels and allocating all the memory to them is the order-optimal scheme. In proving these results, we develop new information-theoretic lower bounds for the problem.Comment: 13 pages; 2 figures. A shorter version is to appear in IEEE ISIT 201

Caching with Partial Adaptive Matching

We study the caching problem when we are allowed to match each user to one of a subset of caches after its request is revealed. We focus on non-uniformly popular content, specifically when the file popularities obey a Zipf distribution. We study two extremal schemes, one focusing on coded server transmissions while ignoring matching capabilities, and the other focusing on adaptive matching while ignoring potential coding opportunities. We derive the rates achieved by these schemes and characterize the regimes in which one outperforms the other. We also compare them to information-theoretic outer bounds, and finally propose a hybrid scheme that generalizes ideas from the two schemes and performs at least as well as either of them in most memory regimes.Comment: 35 pages, 7 figures. Shorter versions have appeared in IEEE ISIT 2017 and IEEE ITW 201