In this thesis, we examine resource allocation strategies for massive multiple-input and multiple-output (MIMO) based wireless communications systems to increase system performance, considering computation-intensive applications with low-latency communication. Firstly, we propose user selection algorithms for non-orthogonal multiple-access (NOMA)-based massive MIMO systems in densely deployed scenarios to increase the sum data rate. Then, we investigate mobile edge computing (MEC) as a solution to enable computation-intensive and delay-critical applications. We propose resource allocation algorithms considering the downlink and uplink transmit powers, the task offloading decision factor and the computing resources to reduce both transmission and computing delays for the massive MIMO-NOMA-assisted MEC system. Finally, we consider a cooperative MEC system where helpers assist in the execution of cell-edge users' computation-intensive tasks with low latency. On the other hand, the task offloading in MEC can introduce security concerns as the offloaded data may be intercepted and overheard by eavesdroppers. Since ensuring a secure task offloading scheme in MEC is important, we formulate the optimization problem to minimize both offloading and computing delays while satisfying security constraints for a massive MIMO-based cooperative MEC. We provide performance results based on sum data rate, delay and total offloading data for the proposed schemes in massive MIMO based wireless communication systems.