ed for both computing and communication in such a scenario, we propose the metric computation efficiency, which is defined as the number of total com- puted bits divided by the energy consumed. We argue that this metric is more appropriate since it can measure how efficient the system is, in terms of computed bits per Joule, for a system involving massive computation needs. Our work expands [199] and [209] in two major aspects. Firstly, we consider maximizing the computation efficiency instead of purely maximizing computed data bits or minimizing energy consumption compared with [199]. Secondly, we combine local computing and data offloading in a hybrid approach instead of offloading only [209]. This chapter presents the following advances in the MEC offloading system. 1. We propose a new performance metric in MEC networks: computation efficiency, which is defined as the number of computed bits divided by the corresponding energy con- sumption. Computation efficiency can drive toward efficient on-board power utilization while achieving satisfactory QoS. 2. The fundamental trade-off between local computing and data offloading is analyzed. Results show that with practical parameter settings, when data size is small, more data will be processed locally. But when the data grows, offloading will play a more important role in improving the computation efficiency. 7.2 System Model In this chapter, we consider a downlink MEC network which consists of one MEC server and K randomly located UEs [173]. The server has a single antenna and SO does each UE. Assume the channel between the server and the UE is a block-fading-based model, i.e. the channel remains constant during a time slot with length T but varies from time to time. The channel state information is assumed to be available at the server. At the beginning of a particular time T, each UE has a computation-intensive task to compute. Due to the com- putation resource limit or power limit or both, these tasks are offloaded to the nearby MEC server for a more powerful p