Internet of Things (IoT) is producing an extraordinary volume of data daily, and it is possible that the data may become useless while on its way to the cloud, due to long distances. Fog/edge computing is a new model for analysing and acting on time-sensitive data, adjacent to where it is produced. Further, cloud services provided by large companies such as Google, can also be localised to improve response time and service agility. This is accomplished through deploying small-scale datacentres in various locations, where needed in proximity of users; and connected to a centralised cloud that establish a multi-access edge computing (MEC). The MEC setup involves three parties, i.e., service providers (IaaS), application providers (SaaS), network providers (NaaS); which might have different goals, therefore, making resource management difficult. Unlike existing literature, we consider resource management with respect to all parties; and suggest game-theoretic resource management techniques to minimise infrastructure energy consumption and costs while ensuring applications’ performance. Our empirical evaluation, using Google’s workload traces, suggests that our approach could reduce up to 11.95 percent energy consumption, and$\sim$17.86% user costs with negligible loss in performance. Moreover, IaaS can reduce up to 20.27 percent energy bills and NaaS can increase their costs-savings up to 18.52 percent as compared to other methods.