Social-Assisted Two-Stage Cooperative Offloading and Resource Allocation for Mobile Edge Computing Networks: A Stackelberg Game and Hybrid Actor-Critic Based Approach
Yuting Li, Zhiwei Wei, Xingcheng Liu, Yitong Liu, Yi Xie, Guangjie Han · IEEE Internet of Things Journal · 2025
Mobile Edge Computing (MEC) is a promising technology for future 6G communication systems. However, the dynamic network environment and the selfish nature of devices pose challenges to task offloading. Therefore, it is very critical to design an effective cooperative offloading scheme in dynamic environments. In this paper, a social-assisted two-stage cooperative task offloading and resource allocation algorithm based on Stackelberg game and DRL (SAC-SDRL) is proposed to maximize the system utility. The problem is formulated as a mixed integer non-linear programming (MINLP) problem that jointly determined the edge server selection, and offloading rate, resource price, and resource allocation. To address this problem, two stage-solutions are introduced. In the first stage, given a fixed resource price and offloading rate, the edge server selection and resource allocation scheme based on hybrid actor-critic algorithm is designed to solve the problem of hybrid action space. In order to avoid invalid decision space, a clustering method based on social relationship and spectral clustering is developed. In the second stage, based on the obtained edge server selection and resource allocation decision, a dynamic pricing and offloading incentive scheme based on the Stackelberg game is proposed, in which the optimal resource price and optimal offloading rate can be determined with the proposed gradient-based iterative search method. Moreover, it is proved that the game can achieve the Stackelberg equilibrium. Finally, simulation results show that the proposed SAC-SDRL algorithm can achieve higher system utility compared with other concerned algorithms.