In the conventional centralized networking architecture, content required by mobile users is obtained from distant Internet datacenters before being allocated on the mobile core network. This paradigm not only leads to content transmission delays but also imposes high bandwidth pressure on backhaul links, probably causing network congestion. Recently, cooperative edge caching technology is believed to be highly effective in addressing the above challenges. Nevertheless, it remains to be a great difficulty to properly decide how, when, and where to cache content over mobile edge computing (MEC) terminals. In this paper, we propose DCPS, a social Community-Interest-Aware Centralized Resource Scheduling Method. The proposed framework employs a Community Detection model and an Allocation (CDA) one to cluster users that are both socially connected and within geographic proximity. It assigns the communities formed by the clustering model to the most suitable base stations for content placement accordingly. Furthermore, it employs an Incremental Sequence Prediction Algorithm (IMSR) to predict user mobility as well as future content preferences for dynamic community adjustments with the help of a Reinforcement Learning-based Centralized Actor-Critic (CACP) algorithm. Simulations upon real-world datasets clearly demonstrate that the proposed algorithm outperforms existing methods across multiple performance metrics.