RAPTOR: Rate-Adaptive Pricing and Optimal Resource Allocation in Serverless Edge Computing

Edge computing(EC) is emerging as a key enabler for latency-sensitive applications such asAugmented Reality(AR), autonomous driving, and industrial IoT, by bringing computational resources closer toWireless Devices(WDs). However, the limited computational capacity inherent to EC presents challenges in resource allocation and in designing pricing mechanisms that provide the right incentives and are aligned with the user-perceived service quality. This paper addresses these challenges by formulating a Stackelberg game that models WDs’ valuation of EC services based on their offloading rates and the service quality they receive. We prove the existence of Stackelberg equilibria and we propose a tractable approximation technique based on log-barrier functions for computing approximate equilibria. Furthermore, to overcome computational issues, we build on the concept of a Differential Stackelberg Equilibrium (DSE) and we propose Stackelberg Gradient Play (SGP), an implicit gradient-based algorithm that ensures convergence to DSE while maintaining efficiency. Extensive simulations show that our approach significantly outperforms existing methods, achieving up to 70% higher revenue for the edge operator while reducing computational overhead substantially. These results underscore the viability of our framework for use in EC systems that require fast, adaptive, and service-aware joint resource management and pricing.