2,020 research outputs found
Hierarchical Coded Caching
Caching of popular content during off-peak hours is a strategy to reduce
network loads during peak hours. Recent work has shown significant benefits of
designing such caching strategies not only to deliver part of the content
locally, but also to provide coded multicasting opportunities even among users
with different demands. Exploiting both of these gains was shown to be
approximately optimal for caching systems with a single layer of caches.
Motivated by practical scenarios, we consider in this work a hierarchical
content delivery network with two layers of caches. We propose a new caching
scheme that combines two basic approaches. The first approach provides coded
multicasting opportunities within each layer; the second approach provides
coded multicasting opportunities across multiple layers. By striking the right
balance between these two approaches, we show that the proposed scheme achieves
the optimal communication rates to within a constant multiplicative and
additive gap. We further show that there is no tension between the rates in
each of the two layers up to the aforementioned gap. Thus, both layers can
simultaneously operate at approximately the minimum rate.Comment: 31 page
Content Caching and Delivery over Heterogeneous Wireless Networks
Emerging heterogeneous wireless architectures consist of a dense deployment
of local-coverage wireless access points (APs) with high data rates, along with
sparsely-distributed, large-coverage macro-cell base stations (BS). We design a
coded caching-and-delivery scheme for such architectures that equips APs with
storage, enabling content pre-fetching prior to knowing user demands. Users
requesting content are served by connecting to local APs with cached content,
as well as by listening to a BS broadcast transmission. For any given content
popularity profile, the goal is to design the caching-and-delivery scheme so as
to optimally trade off the transmission cost at the BS against the storage cost
at the APs and the user cost of connecting to multiple APs. We design a coded
caching scheme for non-uniform content popularity that dynamically allocates
user access to APs based on requested content. We demonstrate the approximate
optimality of our scheme with respect to information-theoretic bounds. We
numerically evaluate it on a YouTube dataset and quantify the trade-off between
transmission rate, storage, and access cost. Our numerical results also suggest
the intriguing possibility that, to gain most of the benefits of coded caching,
it suffices to divide the content into a small number of popularity classes.Comment: A shorter version is to appear in IEEE INFOCOM 201
Content Delivery in Erasure Broadcast Channels with Cache and Feedback
We study a content delivery problem in a K-user erasure broadcast channel
such that a content providing server wishes to deliver requested files to
users, each equipped with a cache of a finite memory. Assuming that the
transmitter has state feedback and user caches can be filled during off-peak
hours reliably by the decentralized content placement, we characterize the
achievable rate region as a function of the memory sizes and the erasure
probabilities. The proposed delivery scheme, based on the broadcasting scheme
by Wang and Gatzianas et al., exploits the receiver side information
established during the placement phase. Our results can be extended to
centralized content placement as well as multi-antenna broadcast channels with
state feedback.Comment: 29 pages, 7 figures. A short version has been submitted to ISIT 201
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
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