Mobile Offloading in Vehicular Networks: A RewardBased Incentive Caching Scheme The massive growth of mobile vehicles arises immense mobile traffic demand on the current cellular communication system. To cope with this challenge, a new transmission pattern that vehicle nodes cache some data contents in advance and provide data access for future requests via direct contacts has become an appealing solution for data offloading. However due to the limited resources, the fact that vehicle nodes might not be willing to assist data caching and transfer is plausible. Therefore in this paper, we build a rewardbased cachingoffloading framework, which efficiently offload cellular traffic by exploiting the storage capability and contact opportunity among vehicle nodes. The whole framework is divided into request phase and caching phase, an auction mechanism which comprises the bidding strategy, the responseforbids strategy, and the prediction method is designed in the former phase and the optimal caching decision can be derived by solving a 0xe2x80x931 knapsack problem in the latter phase. We conduct simulations on timeevolving topology traces, which validates the feasibility and efficiency of the proposed offloading paradigm.

Quality of ExperienceAware Mobile Edge Caching through a Vehicular Cloud Densification through small cells and caching in base stations have been proposed to deal with the increasing demand for Internet content and the related overload on the cellular infrastructure. However, these solutions are expensive to install and maintain. Instead, using vehicles acting as mobile caches might represent an interesting alternative. In our work, we assume that users can query nearby vehicles for some time, and be redirected to the cellular infrastructure when the deadline expires. Beyond reducing costs, in such an architecture, through vehicle mobility, a user sees a much larger variety of locally accessible content within only few minutes. Unlike most of the related works on delay tolerant access, we consider the impact on the user experience by assigning different retrieval deadlines per content. In our paper, we provide the following contributions: (i) we model analytically such a scenario; (ii) we formulate an optimization problem to maximize the traffic offloaded while ensuring user experience guarantees; (iii) we propose two variable deadline policies; (iv) we perform realistic tracebased simulations, and we show that, even with low technology penetration rate, more than 60% of the total traffic can be offloaded which is around 20% larger compared to existing allocation policies.