Stackelberg Game-Based Bandwidth Allocation and Resource Pricing for Multiuser in MEC System

With the rapid development of artificial intelligence, a substantial number of computing-intensive applications have emerged in Internet of Things (IoT) devices. The mobile-edge computing (MEC) architecture enables the provision of abundant computing and storage resources in close proximity to end users (EUs), thereby effectively enhancing their Quality of Experience (QoE). Nonetheless, both the MEC server and EUs are self-interests, it is crucial to establish suitable incentive mechanism to promote active engagement from both parties in the offloading process. Therefore, we employ the Stackelberg game to describe the interaction process between EUs and the MEC server, and an optimal relationship between bandwidth and offloading task size is established to simplify the decision problem for EUs. Then, the optimal strategies for the MEC server and EUs are solved using reverse induction. Given the limited resources of the MEC server, we propose a dynamic programming-based resource allocation (DPRA) algorithm to maximize the revenue of the MEC server while ensuring the cost of each EU. The simulation results demonstrate that the DPRA algorithm can reduce latency and energy consumption costs, significantly outperforming other comparative strategies in terms of performance at both EUs and the MEC server.