Communication is a natural way to improve coordination in multi-agent systems under decentralized control. It allows the agents to exchange local information, to increase their observability on the system and thus leading to higher performance. Recent works dealing with decentralized control in cooperative multiagent systems have shown a great interest in Decentralized Markov Decision Processes (DEC-MDPs). However, communication models that are proposed in DEC-MDPs make strong assumptions which seldom hold in realistic multiagent systems where the execution of the agents may be asynchronous, communication is time and resource consuming and may be restricted by temporal constraints. In this paper we propose an approach that allows us to formalize more complex and realistic communication decisions in DEC-MDPs with interaction graph. We assume a communication model where, at each decision step, each agent must be able to decide to communicate or not, which information to communicate and to whom. In order to make such decisions, we extend one of the most scalable decentralized decision model, the DEC-MDP with opportunity cost (OC-DEC-MDP). This new decision model allows us to assess the value of making decisions on when and what communicating and to whom, and to save the performance of OC-DEC-MDPs.