Millimeter wave (mmWave) is a crucial component in 5G and beyond 5G communications. However, the dense deployment of mmWave transceivers would impose a heavy burden on the management of the radio access network (RAN). This challenge increases the need for leveraging intelligent network management techniques. Thanks to edge computing, machine learning (ML) based network management algorithms and other delay-sensitive user applications can operate at the network edge. But, due to the limited resources on edge servers, developing an orchestration scheme for intelligent network management and user applications is necessary. In this paper, we provide an edge-centric resource management framework for intelligent RAN management and applications with the awareness of the users' quality of experiences (QoE). Specifically, we consider the scenario of a mmWave communication system equipped with an ML-based mmWave beam tracking algorithm. The users under this system request mobile edge gaming..