Correlation-Aware Distributed Caching and Coded Delivery

Cache-aided coded multicast leverages side information at wireless edge caches to efficiently serve multiple groupcast demands via common multicast transmissions, leading to load reductions that are proportional to the aggregate cache size. However, the increasingly unpredictable and personalized nature of the content that users consume challenges the efficiency of existing caching-based solutions in which only exact content reuse is explored. This paper generalizes the cache-aided coded multicast problem to a source compression with distributed side information problem that specifically accounts for the correlation among the content files. It is shown how joint file compression during the caching and delivery phases can provide load reductions that go beyond those achieved with existing schemes. This is accomplished through a lower bound on the fundamental rate-memory trade-off as well as a correlation-aware achievable scheme, shown to significantly outperform state-of-the-art correlation-unaware solutions, while approaching the limiting rate-memory trade-off.Comment: In proceeding of IEEE Information Theory Workshop (ITW), 201

Cache-Aided Coded Multicast for Correlated Sources

The combination of edge caching and coded multicasting is a promising approach to improve the efficiency of content delivery over cache-aided networks. The global caching gain resulting from content overlap distributed across the network in current solutions is limited due to the increasingly personalized nature of the content consumed by users. In this paper, the cache-aided coded multicast problem is generalized to account for the correlation among the network content by formulating a source compression problem with distributed side information. A correlation-aware achievable scheme is proposed and an upper bound on its performance is derived. It is shown that considerable load reductions can be achieved, compared to state of the art correlation-unaware schemes, when caching and delivery phases specifically account for the correlation among the content files.Comment: In proceeding of IEEE International Symposium on Turbo Codes and Iterative Information Processing (ISTC), 201

Broadcast Caching Networks with Two Receivers and Multiple Correlated Sources

The correlation among the content distributed across a cache-aided broadcast network can be exploited to reduce the delivery load on the shared wireless link. This paper considers a two-user three-file network with correlated content, and studies its fundamental limits for the worst-case demand. A class of achievable schemes based on a two-step source coding approach is proposed. Library files are first compressed using Gray-Wyner source coding, and then cached and delivered using a combination of correlation-unaware cache-aided coded multicast schemes. The second step is interesting in its own right and considers a multiple-request caching problem, whose solution requires coding in the placement phase. A lower bound on the optimal peak rate-memory trade-off is derived, which is used to evaluate the performance of the proposed scheme. It is shown that for symmetric sources the two-step strategy achieves the lower bound for large cache capacities, and it is within half of the joint entropy of two of the sources conditioned on the third source for all other cache sizes.Comment: in Proceedings of Asilomar Conference on Signals, Systems and Computers, Pacific Grove, California, November 201

On Coding for Cache-Aided Delivery of Dynamic Correlated Content

Cache-aided coded multicast leverages side information at wireless edge caches to efficiently serve multiple unicast demands via common multicast transmissions, leading to load reductions that are proportional to the aggregate cache size. However, the increasingly dynamic, unpredictable, and personalized nature of the content that users consume challenges the efficiency of existing caching-based solutions in which only exact content reuse is explored. This paper generalizes the cache-aided coded multicast problem to specifically account for the correlation among content files, such as, for example, the one between updated versions of dynamic data. It is shown that (i) caching content pieces based on their correlation with the rest of the library, and (ii) jointly compressing requested files using cached information as references during delivery, can provide load reductions that go beyond those achieved with existing schemes. This is accomplished via the design of a class of correlation-aware achievable schemes, shown to significantly outperform state-of-the-art correlation-unaware solutions. Our results show that as we move towards real-time and/or personalized media dominated services, where exact cache hits are almost non-existent but updates can exhibit high levels of correlation, network cached information can still be useful as references for network compression.Comment: To apear in IEEE Journal on Selected Areas in Communication

Cache-Aided Delivery Networks with Correlated Content in a Shared Cache Framework

Internet traffic is growing exponentially due to the penetration of powerful internet-connected devices and cutting-edge technologies. Additionally, the rise in internet usage has coincided with a shift in the nature of data traffic from voice-based to content-based usage, putting significant stress on delivery networks. Despite the infrastructural advancements in communication networks over the past few years, content delivery networks (CDNs) still face challenges in keeping up with the high delivery data rates and suffer from the imbalanced network load between off-peak hours and peak hours. In this regard, content caching has emerged as an efficient technique to combat the high delivery date rates and maintain a balanced network load while improving the quality of services (QoS) by storing some popular content close to the end users. Caching networks operate in two phases; the placement phase during off-peak hours before users reveal their demands and the delivery phase, which is accomplished when users’ demands are revealed to the server during peak hours. As the server is unaware of the demands during the placement phase, this phase must be designed carefully to minimize the delivery rate regardless of the requested content during peak hours. This dissertation studies cache-aided delivery networks with correlated content in a shared cache framework. A shared cache framework is beneficial in the current and next-generation wireless networks as it provides a local cache to all users within small base stations (SBSs), relieving strain on the backhaul. Furthermore, the library of a caching network could consist of content with a high degree of similarity in many practical applications; Therefore, exploiting the similarity among library content can also be leveraged to reduce the delivery rate in such networks. In this dissertation, we look at the proposed caching network from an information-theoretic perspective and formulate it as a distributed source coding problem with side information at the decoder. Then, the critical question arises as to what should be cached as side information to reduce the delivery rate of the network efficiently. To answer this question, we propose an automatic clustering scheme using artificial intelligence (AI)-based optimization techniques to identify the selected side information for the entire library. We comprehensively evaluate the performance of the general clustering framework in a separate chapter by considering different datasets and distance measures. The general clustering framework enables us to develop two novel clustering schemes as a part of the placement phase of the proposed caching networks under different settings throughout this study, considering both the similarity and popularity of the library content. Upon identifying the selected side information for such networks, the next question that should be answered is how we should place the side information into caches; And consequently, what is the delivery strategy for this placement scheme? We have furnished our answer to these questions by considering three different caching networks: first, a network in a single shared cache framework under lossy caching. Next is a network with multiple shared caches under uniform popularity, and finally, a network with multiple shared caches under non-uniform preferences. In such networks, we address the placement and delivery strategy to show the trade-off between the delivery rate and the memory size of the system. We calculate the peak and expected rates of the studied networks by considering the rate-distortion function and caching strategy. We also introduce the optimum library partitioning formulated to minimize the peak delivery rate in the system. The performance analysis and extensive simulations of the proposed solution confirm that our scheme provides a considerable boost in network efficiency compared to legacy caching schemes due to our problem formulation and the careful extraction of side information during the placement phase