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Cache-aided wireless device-to-device (D2D) networks allow significant throughput increase, depending on the concentration of the popularity distribution of files. Many studies assume that all users have the same preference distribution; however, this may not be true in practice. This work investigates whether and how the information about individual preferences can benefit cache-aided D2D networks. We examine a clustered network and derive a network utility that considers both the user distribution and channel fading effects into the analysis. We also formulate a utility maximization problem for designing caching policies. This maximization problem can be applied to optimize several important quantities, including throughput, energy efficiency (EE), cost, and hit-rate, and to solve different tradeoff problems. We provide a general approach that can solve the proposed problem under the assumption that users coordinate, then prove that the proposed approach can obtain the stationary point under a mild assumption. Using simulations of practical setups, we show that performance can improve significantly with proper exploitation of individual preferences. We also show that different types of tradeoffs exist between different performance metrics and that they can be managed through caching policy and cooperation distance designs. 

Recent investigations showed that cache-aided device-to-device (D2D) networks can be improved by properly exploiting the individual preferences of users. Since in practice it might be difficult to make centralized decisions about the caching distributions, this paper investigates the individual preference aware caching policy that can be implemented distributedly by users without coordination. The proposed policy is based on categorizing different users into different reference groups associated with different caching policies according to their preferences. To construct reference groups, learning-based approaches are used. To design caching policies that maximize throughput and hit-rate, optimization problems are formulated and solved. Numerical results based on measured individual preferences show that our design is effective and exploiting individual preferences is beneficial. 

This paper proposes a user scheduling and power allocation method for content delivery in wireless caching helper networks without any stringent constraint on the interference model. For supporting delay-sensitive and time-varying user demands, the actual delivery quantity of the requested content should be dynamically controlled by advanced scheduling and power allocation. In addition, it is difficult for a central unit to control the content delivery due to a lack of knowledge of the entire time-varying network; therefore, a belief-propagation (BP)-based algorithm that facilitates distributed decisions on user scheduling and power allocation at every caching helper is presented. The proposed delivery scheme maximizes power efficiency while limiting the average delay of user request satisfactions by managing interference among users well. Simulation results show that the proposed scheme provides almost the same delay performance as the exhaustively found optimal one at the expense of little power consumption. 

In wireless caching networks, a user generally has a concrete purpose of consuming contents in a certain preferred category, and requests multiple contents in sequence. While most existing research on wireless caching and delivery has focused only on one-shot requests, the popularity distribution of contents requested consecutively is definitely different from the one-shot request and has been not considered. Also, especially from the perspective of the service provider, it is advantageous for users to consume as many contents as possible. Thus, this paper proposes two cache allocation policies for categorized contents and consecutive user demands, which maximize 1) the cache hit rate and 2) the number of consecutive content consumption, respectively. Numerical results show how categorized contents and consecutive content requests have impacts on the cache allocation. 

Abstract—Due to the concentrated popularity distribution of video files, caching of popular files on devices, and distributing them via device-to-device (D2D) communications allows a dramatic increase in the throughput of wireless video networks. However, since the popularity distribution is not static and the caching policy might be outdated, there is a need for replacement of cache content. In this work, by exploiting the broadcasting of the base station (BS), we model the caching content replacement in BS assisted wireless D2D caching networks and propose a practically realizable replacement procedure. Subsequently, by introducing a queuing system, the replacement problem is formulated as a sequential decision making problem, in which the long term average service rate is optimized under average cost constraint and queue stability. We propose a replacement design using Lyapunov optimization, which effectively solves the problem and makes decisions. Using simulations, we evaluate the proposed design. The results clearly indicate that, when dynamics exist, the systems exploiting replacement can significantly outperform the systems using merely the static policy. 

Caching at the wireless edge has proven to be a promising approach for efficient video distribution, especially when aided by device-to-device communication. A widely explored scheme is to sub-divide a cell into clusters, and allow one pair of users within each cluster to communicate in each time slot. As more devices are raising frequent requests for popular videos, activating multiple links simultaneously can potentially improve the throughput. However, allowing multiple links at the same time requires to solve the problems of avoiding request clashes, i.e., multiple users requesting transmission from the same caching node, as well as interference management. To address these issues, this paper proposes new designs of both the caching policy and the transmission policy (i.e., link scheduling and power control). Furthermore, the duration of each time slot is optimized to improve the throughput. Finally, some numerical results demonstrate the performance gain of the proposed designs. 

Caching of video files on user devices, combined with file exchange through device-to-device (D2D) communications is a promising method for increasing the throughput of wireless networks. Previous theoretical investigations showed that throughput can be increased by orders of magnitude, but assumed a Zipf distribution for modeling the popularity distribution, which was based on observations in wired networks. Thus the question whether cache-aided D2D video distribution can provide in practice the benefits promised by existing theoretical literature remains open. To answer this question, we provide new results specifically for popularity distributions of video requests of mobile users. Based on an extensive real-world dataset, we adopt a generalized distribution, known as Mandelbrot-Zipf (MZipf) distribution. We first show that this popularity distribution can fit the practical data well. Using this distribution, we analyze the throughput–outage tradeoff of the cache-aided D2D network and show that the scaling law is identical to the case of Zipf popularity distribution when the MZipf distribution is sufficiently skewed, implying that the benefits previously promised in the literature could indeed be realized in practice. To support the theory, practical evaluations using numerical experiments are provided, and show that the cache-aided D2D can outperform the conventional unicasting from base stations. 

On-demand video accounts for the majority of wireless data traffic. Video distribution schemes based on caching combined with device-to-device (D2D) communications promise order-of-magnitude greater spectral efficiency for video delivery, but hinge on the principle of “concentrated demand distributions." This paper presents, for the first time, the analysis and evaluations of the throughput–outage tradeoff of such schemes based on measured cellular demand distributions. In particular, we use a dataset with more than 100 million requests from the BBC iPlayer, a popular video streaming service in the U.K., as the foundation of the analysis and evaluations. We present an achievable scaling law based on the practical popularity distribution, and show that such scaling law is identical to those reported in the literature. We find that also for the numerical evaluations based on a realistic setup, order-of-magnitude improvements can be achieved. Our results indicate that the benefits promised by the caching-based D2D in the literature could be retained for cellular networks in practice.