Abstract
In order to offload network traffic, we design a device caching strategy by jointly considering a popularity model, social influence and incentive design in this paper. Firstly, we propose a prediction model by virtue of users' social network information to evaluate users' encounter probability. Moreover, users' content preference is predicted using users' context information. Based on these predicted values, a content placement algorithm is described provided that the users will fully cooperate to optimize system performance. Thereafter, a more practical scenario where users are selfish and unwilling to devote their resources is considered. A Stackelberg game is established between the mobile network operator (MNO) and users by providing an incentive to encourage cooperation. Device caching strategy and incentive price design are determined by analyzing the Stackelberg game and finding the Stackelberg equilibrium point. We verify the effectiveness of our prediction models utilizing real data sets. Simulation results show that the cache hit ratio can be considerably improved by exploiting social and context information. Incentive design and profit analysis are also thoroughly investigated.
Highlights
It is revealed by the latest report from Cisco that the number of mobile Internet user equipments is anticipated to achieve as many as threefold the global population
By combining content placement and incentive mechanism design, we propose a non-cooperative content placement scheme based on the predicted D2D transmitters encounter probability and user preference
CONTRIBUTION The aforementioned works on D2D caching considering social awareness or Stackelberg game approach overlook the crucial impact of the individual user preference in content requests on content placement design or assume that the preferences of every user are perfectly known as common knowledge within the network
Summary
It is revealed by the latest report from Cisco that the number of mobile Internet user equipments is anticipated to achieve as many as threefold the global population. By combining content placement and incentive mechanism design, we propose a non-cooperative content placement scheme based on the predicted D2D transmitters encounter probability and user preference. B. CONTRIBUTION The aforementioned works on D2D caching considering social awareness or Stackelberg game approach overlook the crucial impact of the individual user preference in content requests on content placement design or assume that the preferences of every user are perfectly known as common knowledge within the network. PROBLEM FORMULATION To achieve maximum quality from edge caching, we choose to maximize the total hit ratio, with constraints on user devices’ storage capacity This total hit ratio is defined as the probability that a typical user can retrieve the requested content from one of the user devices through D2D communication. We first analyze the encounter probability of users by virtue of social networks and predict individual user preference for popular contents
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