Differentiated QoE-oriented task offloading and resource allocation scheme based on matching theory

With the rapid development of 6G communication networks and mobile edge computing(MEC) technology, the deployment density of base stations equipped with edge servers has been continuously increasing, and computational tasks show a growing trend toward diversification. To address the impact of differentiated quality of experience (QoE) for users and unbalanced resource allocation on system performance, an efficient matching offloading scheme was proposed. The scheme comprehensively considered both offloading decisions and computational resource allocation, and a mixed-integer nonlinear programming problem was formulated with the objective of maximizing system utility. By decomposing the original problem, an iterative optimization algorithm was designed based on bilateral matching theory for solution. Simulation experiments were conducted using public datasets from the central business district (CBD) of Melbourne, Australia. The results demonstrate that, compared with existing schemes, the proposed scheme shows significant advantages in improving system utility.