Machine Learning Assisted Video Stream Offloading for 5G MBMS Mobile Edge Computing

With the advancement of multimedia technology, 5G MBMS has become a promising technology that makes full and effective use of network resources to provide services. Mobile edge computing (MEC) is a key technology that brings important improvements to MBMS by utilizing contextual information such as multimedia, virtual reality, etc. However, the deployment of MEC may bring the latency to the network, which requires the deployment consideration of computing nodes. Moreover, mobile network operators need to consider the number of base stations required to accommodate the upcoming MBMS data streams. In this paper, we propose a computational offloading strategy and a co-optimization mechanism of video resource assignment in an edge cloud server enabled MBMS service system. Our approach aims to maximize computation speed while satisfying limited resource constraints, constrained energy consumption, and minimum latency time requirements, in order to provide better MBMS services. Additionally, our approach offers an advanced solution for achieving optimal computational offloading and resource allocation in edge cloud server enabled MBMS service systems. Extensive simulation results demonstrate that the proposed mechanism outperforms current state-of-the-art schemes. Specifically, the total calculation rate of the large-scale cloud server is significantly reduced under energy constraints, total delay requirements, and different network parameters.