  In this paper, a novel proximity and load-aware resource allocation for
vehicle-to-vehicle (V2V) communication is proposed. The proposed approach
exploits the spatio-temporal traffic patterns, in terms of load and vehicles'
physical proximity, to minimize the total network cost which captures the
tradeoffs between load (i.e., service delay) and successful transmissions while
satisfying vehicles's quality-of-service (QoS) requirements. To solve the
optimization problem under slowly varying channel information, it is decoupled
the problem into two interrelated subproblems. First, a dynamic clustering
mechanism is proposed to group vehicles in zones based on their traffic
patterns and proximity information. Second, a matching game is proposed to
allocate resources for each V2V pair within each zone. The problem is cast as
many-to-one matching game in which V2V pairs and resource blocks (RBs) rank one
another in order to minimize their service delay. The proposed game is shown to
belong to the class of matching games with externalities. To solve this game, a
distributed algorithm is proposed using which V2V pairs and RBs interact to
reach a stable matching. Simulation results for a Manhattan model shown that
the proposed scheme yields a higher percentage of V2V pairs satisfying QoS as
well as significant gain in terms of the signal-to-interference-plus-noise
ratio (SINR) as compared to a state-of-art resource allocation baseline.
