The recent surge of applications involving the use of @math video challenges mobile networks infrastructure, as @math video files are of significant size, and current delivery and edge caching architectures are unable to guarantee their timely delivery. In this paper, we investigate the problem of joint collaborative content-aware caching and delivery of @math videos. The proposed scheme takes advantage of @math video encoding in multiple tiles and layers to make fine-grained decisions regarding which tiles to cache in each Small Base Station (SBS), and from where to deliver them to the end users, as users may reside in the coverage area of multiple SBSs. This permits to cache the most popular tiles in the SBSs, while the remaining tiles may be obtained through the backhaul. In addition, we explicitly consider the time delivery constraints to ensure continuous video playback. To reduce the computational complexity of the optimization problem, we simplify it by introducing a fairness constraint. This allows us to split the original problem into subproblems corresponding to Groups of Pictures (GoP). Each of the subproblems is then decoupled into their caching and routing components and solved with the method of Lagrange partial relaxation. Finally, we evaluate the performance of the proposed method for various system parameters and compare it with schemes that do not consider @math video encoding into multiple tiles and quality layers. The results make clear the benefits coming from caching and delivery decisions on per tile basis.
Edge caching has been proposed as an efficient way to improve the performance of cellular networks in terms of the observed delivery delay @cite_25 @cite_33 @cite_38 @cite_22 @cite_13 @cite_17 , energy consumption @cite_20 , and operating cost @cite_9 . These benefits of edge caching are attributed to the fact that only a small portion of available content accounts for most of the traffic load @cite_19 . Hence, by caching the most popular content at the SBSs, significant resources are saved. In @cite_30 , it is explored how to capture content popularity dynamics among the various caches by using at each SBS the information about its neighboring SBSs. The impact of content request patterns and users' behavior on edge caching-assisted mobile video streaming of real-life data sets is studied in @cite_10 .
@cite_30 @cite_38 @cite_22 @cite_33 @cite_10 @cite_9 @cite_19 @cite_13 @cite_25 @cite_20 @cite_17
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Caching at the edge is a promising technique to cope with the increasing data demand in wireless networks. This paper analyzes the performance of cellular networks consisting of a tier macro-cell wireless backhaul nodes overlaid with a tier of cache-aided small cells. We consider both static and dynamic association policies for content delivery to the user terminals and analyze their performance. In particular, we derive closed-form expressions for the area spectral efficiency and the energy efficiency, which are used to optimize relevant design parameters such as the density of cache-aided small cells and the storage size. By means of this approach, we are able to draw useful design insights for the deployment of highly performing cache-aided tiered networks. To alleviate the pressure brought by the explosion of mobile video traffic on present cellular networks, small cell base stations (SBSs) with caching ability are introduced. In this letter, we consider the caching strategy of scalable video coding streaming over heterogeneous wireless network containing SBSs. We formulate an integer programming problem to minimize the average download time under the constraint of cache size at each SBS, and show that finding the optimal caching placement strategy is NP-hard. Heuristic solution was proposed based on the convex programming relaxation, which reveals the structural properties of cache allocation for each video. Simulation results demonstrate that our proposed caching strategies acquire significant performance gain compared with conventional caching policy. Caching has emerged as a vital tool in modern communication systems for reducing peak data rates by allowing popular files to be pre-fetched and stored locally at end users' devices. With the shift in paradigm from homogeneous cellular networks to the heterogeneous ones, the concept of data offloading to small cell base stations (sBS) has garnered significant attention. Caching at these small cell base stations has recently been proposed, where popular files are pre-fetched and stored locally in order to avoid bottlenecks in the limited capacity backhaul connection link to the core network. In this paper, we study distributed caching strategies in such a heterogeneous small cell wireless network from a reinforcement learning perspective. Using state of the art results, it can be shown that the optimal joint cache content placement in the sBSs turns out to be a NP-hard problem even when the sBS's are aware of the popularity profile of the files that are to be cached. To address this problem, we propose a coded caching framework, where the sBSs learn the popularity profile of the files (based on their demand history) via a combinatorial multi-armed bandit framework. The sBSs then pre-fetch segments of the Fountain-encoded versions of the popular files at regular intervals to serve users' requests. We show that the proposed coded caching framework can be modeled as a linear program that takes into account the network connectivity and thereby jointly designs the caching strategies. Numerical results are presented to show the benefits of the joint coded caching technique over naive decentralized cache placement strategies. Anticipating multimedia file requests via caching at the small cell base stations (SBSs) of a cellular network has emerged as a promising technique for optimizing the quality of service (QoS) of wireless user equipments (UEs). However, developing efficient caching strategies must properly account for specific small cell constraints, such as backhaul congestion and limited storage capacity. In this paper, we address the problem of devising a usercell association, in which the SBSs exploit caching capabilities to overcome the backhaul capacity limitations and enhance the users’ QoS. In the proposed approach, the SBSs individually decide on which UEs to service based on both content availability and on the data rates they can deliver, given the interference and backhaul capacity limitations. We formulate the problem as a oneto-many matching game between SBSs and UEs. To solve this game, we propose a distributed algorithm, based on the deferred acceptance scheme, that enables the players (i.e., UEs and SBSs) to self-organize into a stable matching, in a reasonable number of algorithm iterations. Simulation results show that the proposed cell association scheme yields significant gains, reaching up to 21 improvement compared to a traditional cell association techniques with no caching considerations.