Small cell densification is one of the effective ideas addressing the demand for higher capacity in cellular networks. The major problem faced in such networks is the wireless backhaul link and its limited capacity. Caching most popular files in the memories of small cells base station (SBSs) is an effective solution to this problem. One of the main challenges in caching is choosing the files that are going to be stored in the memory of SBS. In this paper, we model the described caching problem as a graph. This graph is divided into four sub-graphs including placement, access, SBSs and delivery graphs. By making some modifications to the SBS graph, we convert it to a graph that can be colored. Coloring of the generated graph is NP-hard and we use an algorithm proposed in graph-coloring area to color it. To overcome the complexity of above coloring technique, we then propose a simple graph-coloring method based on two point processes, Matern Core-type I and II. We model our network with a new weighted graph which simply can be colored and after that the files are cached accordingly. We evaluate the performance of our proposed methods through simulations. Our results show that by employing out proposed method in a typical considered SBSs network, the load on the macro base station can be reduced by around @math , at the distribution parameter of popularity of files equals to @math , compared to conventional policy which caches the most popular content every where.
Content delivery success in wireless caching helper networks depends mainly on cache-based channel selection diversity and network interference. For given channel fading and network geometry, both channel selection diversity and network interference dynamically vary according to what and how the caching helpers cache at their finite storage space. We study probabilistic content placement (or caching placement) to desirably control cache-based channel selection diversity and network interference in a stochastic wireless caching helper network, with sophisticated considerations of wireless fading channels, interactions among multiple users, such as interference and loads at caching helpers, and arbitrary memory size. Using stochastic geometry, we derive optimal caching probabilities in the closed form to maximize the average success probability of content delivery and propose an efficient algorithm to find the solution in a noise-limited network. In an interference-limited network, based on a lower bound of the average success probability of content delivery, we find near-optimal caching probabilities in the closed form to control the channel selection diversity and the network interference. We numerically verify that the proposed content placement is superior to other comparable content placement strategies. We consider a mobile content delivery network (mCDN) in which special mobile devices designated as caching servers (caching-server device: CSD) can provide mobile stations with popular contents on demand via device-to-device (D2D) communication links. On the assumption that mobile CSD's are randomly distributed by a Poisson point process (PPP), an optimization problem is formulated to determine the probability of storing the individual content in each server in a manner that minimizes the average caching failure rate. Further, we present a low-complexity search algorithm, optimum dual-solution searching algorithm (ODSA), for solving this optimization problem. We demonstrate that the proposed ODSA takes fewer iterations, on the order of О (log N) searches, for caching N contents in the system to find the optimal solution, as compared to the number of iterations in the conventional subgradient method, with an acceptable accuracy in practice. Furthermore, we identify the important characteristics of the optimal caching policies in the mobile environment that would serve as a useful aid in designing the mCDN.  In wireless cloud storage systems, the recovery failure probability depends on not only wireless channel conditions but also storage size of each distributed storage node. For an efficient utilization of limited storage capacity and the performance characterization of allocation strategies, we asymptotically analyze the recovery failure probability of a wireless cloud storage system with a sum storage capacity constraint for both high signal-to-noise ratio (SNR) regime and low SNR regime. Then, we find the optimal storage allocation strategy across distributed storage nodes in terms of the asymptotic recovery failure probability. Our analysis reveals that the maximal symmetric allocation is optimal for high SNR regime and the minimal allocation (with @math complete storage nodes and an incomplete storage node) is optimal for low SNR regime, where @math is the sum storage capacity. Based on the numerical investigation, we also show that in intermediate SNR regime, a balance allocation between the minimal allocation and the maximal symmetric allocation would not be required if we select one between them according to SNR. Departing from the conventional cache hit optimization in cache-enabled wireless networks, we consider an alternative optimization approach for the probabilistic caching placement in stochastic wireless D2D caching networks taking into account the reliability of D2D transmissions. Using tools from stochastic geometry, we provide a closed-form approximation of cache-aided throughput, which measures the density of successfully served requests by local device caches, and we obtain the optimal caching probabilities via numerical optimization. Compared with the cache-hit-optimal case, the optimal caching probabilities obtained by cache-aided throughput optimization show notable gain in terms of the density of successfully served user requests, particularly in dense user environments.  In this paper, we devise the optimal caching placement to maximize the offloading probability for a two-tier wireless caching system, where the helpers and a part of users have caching ability. The offloading comes from the local caching, D2D sharing and the helper transmission. In particular, to maximize the offloading probability we reformulate the caching placement problem for users and helpers into a difference of convex (DC) problem which can be effectively solved by DC programming. Moreover, we analyze the two extreme cases where there is only help-tier caching network and only user-tier. Specifically, the placement problem for the helper-tier caching network is reduced to a convex problem, and can be effectively solved by the classical water-filling method. We notice that users and helpers prefer to cache popular contents under low node density and prefer to cache different contents evenly under high node density. Simulation results indicate a great performance gain of the proposed caching placement over existing approaches. In this paper, we introduce distributed caching of videos at the base stations of the Radio Access Network (RAN) to significantly improve the video capacity and user experience of mobile networks. To ensure effectiveness of the massively distributed but relatively small-sized RAN caches, unlike Internet content delivery networks (CDNs) that can store millions of videos in a relatively few large-sized caches, we propose RAN-aware reactive and proactive caching policies that utilize User Preference Profiles (UPPs) of active users in a cell. Furthermore, we propose video-aware backhaul and wireless channel scheduling techniques that, in conjunction with edge caching, ensure maximizing the number of concurrent video sessions that can be supported by the end-to-end network while satisfying their initial delay requirements and minimize stalling. To evaluate our proposed techniques, we developed a statistical simulation framework using MATLAB and performed extensive simulations under various cache sizes, video popularity and UPP distributions, user dynamics, and wireless channel conditions. Our simulation results show that RAN caches using UPP-based caching policies, together with video-aware backhaul scheduling, can improve capacity by 300 compared to having no RAN caches, and by more than 50 compared to RAN caches using conventional caching policies. The results also demonstrate that using UPP-based RAN caches can significantly improve the probability that video requests experience low initial delays. In networks where the wireless channel bandwidth may be constrained, application of our videoaware wireless channel scheduler results in significantly (up to 250 ) higher video capacity with very low stalling probability.  Video on-demand streaming from Internet-based servers is becoming one of the most important services offered by wireless networks today. In order to improve the area spectral efficiency of video transmission in cellular systems, small cells heterogeneous architectures (e.g., femtocells, WiFi off-loading) are being proposed, such that video traffic to nomadic users can be handled by short-range links to the nearest small cell access points (referred to as “helpers”). As the helper deployment density increases, the backhaul capacity becomes the system bottleneck. In order to alleviate such bottleneck we propose a system where helpers with low-rate backhaul but high storage capacity cache popular video files. Files not available from helpers are transmitted by the cellular base station. We analyze the optimum way of assigning files to the helpers, in order to minimize the expected downloading time for files. We distinguish between the uncoded case (where only complete files are stored) and the coded case, where segments of Fountain-encoded versions of the video files are stored at helpers. We show that the uncoded optimum file assignment is NP-hard, and develop a greedy strategy that is provably within a factor 2 of the optimum. Further, for a special case we provide an efficient algorithm achieving a provably better approximation ratio of 1-(1-1 d )d, where d is the maximum number of helpers a user can be connected to. We also show that the coded optimum cache assignment problem is convex that can be further reduced to a linear program. We present numerical results comparing the proposed schemes.