The surge in video traffic and shift toward on-demand content consumption is straining mobile operators' networks to a breaking point. In this article, we investigate the problem of mobile data offloading for beyond $4$G networks from a caching perspective. Leveraging notions of prediction, storage, and social networking, it is shown that peak traffic demands can be substantially reduced by proactively serving predictable user demands, through caching at the network edge (i.e., base stations and users' devices). Notably, we focus on two caching scenarios which exploit the spatial and social structure of the network. Firstly, in order to alleviate backhaul congestion, we propose a mechanism whereby files are proactively cached during off-peak demands based on file popularity and correlations among users-files patterns. Secondly, leveraging social networks and device-to-device (D2D) communications, we propose a procedure that exploits the social structure of the network by predicting the set of influential users to cache strategic contents and disseminate them among their social ties. Numerical results show that important gains are incurred, with backhaul savings and a higher ratio of satisfied users of up to $22\%$ and $26\%$, respectively. Higher gains can further be obtained by increasing the storage capability at the network edge.