Mobile edge computing (MEC) is an emerging technology that leverages computing, storage, and network resources deployed in the proximity of users to offload terminals from computation intensive and delay-sensitive tasks. In this article, a vehicular MEC system is studied where edges, such as MEC servers deployed at roadside units (RSUs), and vehicles with excessive computing resources are able to provide offloading opportunities to other vehicles having limited computation capabilities. We first review the latest MEC research. Then, we focus on the task of offloading in an incomplete information environment and model the interactions between tasks and edges as a matching game. Our main objective is to minimize the average delay while taking into account vehicle mobility and energy consumption constraints. We elaborate on two typical application scenarios, namely, interference-free orthogonal multipleaccess (OMA) networks and interference nonorthogonal multiple-access (NOMA) networks, and develop two separate heuristic algorithms to solve the delay minimization problem. Simulation results demonstrate the efficiency of the proposed algorithms.