  Vehicular social networking is an emerging application of the promising
Internet of Vehicles (IoV) which aims to achieve the seamless integration of
vehicular networks and social networks. However, the unique characteristics of
vehicular networks such as high mobility and frequent communication
interruptions make content delivery to end-users under strict delay constrains
an extremely challenging task. In this paper, we propose a social-aware
vehicular edge computing architecture that solves the content delivery problem
by using some of the vehicles in the network as edge servers that can store and
stream popular content to close-by end-users. The proposed architecture
includes three components. First, we propose a social-aware graph pruning
search algorithm that computes and assigns the vehicles to the shortest path
with the most relevant vehicular content providers. Secondly, we use a
traffic-aware content recommendation scheme to recommend relevant content
according to their social context. This scheme uses graph embeddings in which
the vehicles are represented by a set of low-dimension vectors (vehicle2vec) to
store information about previously consumed content. Finally, we propose a Deep
Reinforcement Learning (DRL) method to optimize the content provider vehicles
distribution across the network. The results obtained from a realistic traffic
simulation show the effectiveness and robustness of the proposed system when
compared to the state-of-the-art baselines.
