Quality and Access in Wireless Networks: A Game-Theoretic Optimization Approach

The rapid proliferation of wireless networks has revolutionized how individuals and organizations access information and communicate. However, meeting the ever-growing demand for high-quality service and equitable access across heterogeneous user populations remains an enduring challenge. Existing solutions such as adaptive bitrate streaming (ABR), distributed rate-limiting algorithms, multi-server cloud-assisted architectures, and rural network deployment strategies each tackle specific facets of this complex problem, but lack a unified optimization framework that explicitly balances competing objectives: maximizing Quality of Experience (QoE), ensuring fairness in access, and controlling operational costs. In this paper, we propose a comprehensive game-theoretic optimization approach for managing quality and access in wireless networks under constrained resources and dynamic demand. Drawing insights from recent advances in ABR for both constant and variable bitrate encodings, distributed flow control in cloud-based services, multipath redundancy in 5G vehicular communications, and innovative rural connectivity schemes such as Direct-to-Mobile (D2M) broadcasting, we model the interaction between service providers and users as a hierarchical Stackelberg game. In our model, the provider acts as the leader by setting pricing and bandwidth allocation policies to maximize revenue and maintain network stability, while users act as followers who adapt their bitrate demands to maximize their individual QoE under imposed constraints and data caps. This framework explicitly incorporates fairness considerations through the inclusion of the Jain fairness index as a constraint in the provider’s optimization problem. We derive closed-form equilibrium strategies for both the provider and the users, characterizing the conditions under which the system achieves efficient and fair resource allocation. Our simulations demonstrate that the proposed approach improves average Peak Signal-to-Noise Ratio (PSNR) and fairness, reduces stalls, and achieves higher bandwidth utilization compared to conventional single-server and distributed rate-limiting approaches. Furthermore, our framework is extensible to scenarios involving heterogeneous traffic, multipath redundancy, and access in underserved rural regions, making it applicable to a wide range of deployment contexts. This work provides a theoretical foundation and practical guidelines for designing next-generation wireless networks that can sustainably deliver high-quality and equitable services even under tight resource and competitive market conditions. We believe that the proposed framework can inform policymaking, infrastructure investment decisions, and the design of adaptive network protocols, contributing to the advancement of inclusive and resilient wireless communication ecosystems.