Evidence indicates that demands from mobile users (MU) on popular cloud content, e.g., video clips, account for a dramatic increase in data traffic over cellular networks. The repetitive downloading of hot content from cloud servers will inevitably bring a vast quantity of redundant data transmissions to networks. A strategy of distributively pre-storing popular cloud content in the memories of small-cell base stations (SBS), namely, small-cell caching, is an efficient technology for reducing the communication latency whilst mitigating the redundant data streaming substantially. In this paper, we establish a commercialized small-cell caching system consisting of a network service provider (NSP), several video providers (VP), and randomly distributed MUs. We conceive this system in the context of 5G cellular networks, where the SBSs are ultra-densely deployed with the intensity much higher than that of the MUs. In such a system, the NSP, in charge of the SBSs, wishes to lease these SBSs to the VPs for the purpose of making profits, whilst the VPs, after pushing popular videos into the rented SBSs, can provide faster local video transmissions to the MUs, thereby gaining more profits. Specifically, we first model the MUs and SBSs as two independent Poisson point processes, and develop, via stochastic geometry theory, the probability of the specific event that an MU obtains the video of its choice directly from the memory of an SBS. Then, with the help of the probability derived, we formulate the profits of both the NSP and the VPs. Next, we solve the profit maximization problem based on the framework of contract theory, where the NSP acts as a monopolist setting up the optimal contract according to the statistical information of the VPs. Incentive mechanisms are also designed to motivate each VP to choose a proper resource-price item offered by the NSP. Numerical results validate the effectiveness of our proposed contract framework for the commercial caching system.

A large number of computation-hungry mobile applications have led to an ever-increasing computation demands. Mobile edge computing (MEC) has been considered as an emerging paradigm to alleviate the demand effectively by offloading the computationally intensive tasks from mobile devices (MD) to the adjacent MEC servers. It is expected that the quality of computation experience, e.g., the computing energy and the execution latency, can be greatly improved by the MEC. In this paper, we will investigate the computing task offloading problem via the MEC in the context of small-cell base-station (SBS) networks, where each SBS is equipped with an MEC server. Specifically, we first formulate the optimization problem to minimize the objective, i.e., the weighted sum of energy consumption and execution duration. The parameters to be optimized are the allocations of the MD's tasks to be offloaded to the MEC servers. Then we propose a novel belief propagation (BP) algorithm to optimize the task allocation in a distributed manner. Next, we develop the factor graph according to the network topology and decompose the object function into multiple local utilities to fit the factor graph. Finally, we transform local utilities into the estimations of marginal distributions and propose a distributed BP algorithm to solve the estimations. Simulations demonstrate that our BP algorithm can effectively approach the optimal solutions via exhaustive search.

Wireless caching is an efficient mechanism for reducing downloading delay and reducing the traffic pressure over backhual channels by caching some popular content, e.g., video clips, in small base stations (SBSs). In this paper, we consider a commercialized small-cell caching system consisting of a network service provider (NSP), several video retailers (VRs) and mobile users (MUs). The NSP leases its SBSs to VRs in order to earn profits, while the VRs store popular videos into the lent SBSs, thereby gaining profits from providing better services to the MUs. We conceive the system within the framework of contract theory by designing the optimal quality-price contract. We establish the profit function of NSP and VRs and solve the profit maximization problem through contract theory. Numerical results validate the effectiveness of our incentive mechanism for the system.