A MULTI-STAGE GAME-THEORETIC FRAMEWORK FOR TRAFFIC OFFLOADING IN 5/6G NETWORKS: A QUADRATIC UTILITY-BASED CONGESTION MANAGEMENT APPROACH FOR MEGACITIES

This paper proposes an advanced multi-stage game-theoretic framework for congestion manage- ment in dense urban environments, with a focus on 5/6G cellular networks supporting real-time applications such as ultra-high-definition video conferencing. Motivated by the ever-increasing data demand in modern megacities, we introduce a novel Quadratic Utility Stackelberg Of- floading (QUSO) approach. The macro base station (MBS) plays the role of leader, announcing differentiated incentives for high-bandwidth traffic, while multiple Wi-Fi or femtocell access points (APs) act as followers responding to these incentives. Unlike linear models, the pro- posed approach leverages quadratic utility functions that model heterogeneous payoff behav- iors, balancing the network operator’s congestion-reduction benefits against the APs’ costs and operational constraints. Through an in-depth exploration of dynamic incentive schemes, best-response formulations, and equilibrium uniqueness proofs, we demonstrate that the QUSO approach yields signifi- cantly higher throughput, lower latency, and reduced energy consumption compared to uniform- incentive offloading strategies. We further embed extensive mathematical derivations, pseudo- code for iterative algorithm design, and graph-based system layouts to capture real-world com- plexities. A case study in a highly congested metropolitan region illustrates how peak and off-peak loads can be handled by adaptive price adjustments that reward APs for offloading data-heavy flows such as interactive video calls. The results highlight superior Quality of Ser- vice (QoS), energy efficiency, and scalability, underscoring the method’s potential as a blueprint for next-generation cellular networks operating in population-dense, traffic-intensive environ- ments.