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.
ASJC Scopus subject areas
- Computer Networks and Communications
- Electrical and Electronic Engineering