A QoE-Driven Personalized Incentive Mechanism Design for AIGC Services in Resource-Constrained Edge Networks

2025-08-22

AI Summary

To address the conflict between users’ personalized Quality of Experience (QoE) requirements and service providers’ constrained computation and communication resources for AIGC services in edge networks, this paper proposes a multidimensional QoE metric integrating generation accuracy, token count, and latency. We formulate an equilibrium-constrained optimization framework based on Stackelberg game theory and design a dual-perturbation reward optimization algorithm to enable low-overhead dynamic incentive alignment and resource matching. Our key contributions are: (i) the first incorporation of multidimensional QoE into an Equilibrium Problem with Equilibrium Constraints (EPEC) formulation, overcoming limitations of conventional single-dimensional metrics; and (ii) a significant reduction in pricing complexity and system overhead. Experiments demonstrate that, compared to baseline methods, our approach reduces average computation and communication overhead by 64.9%, lowers user service cost by 66.5%, and decreases provider resource consumption by 76.8%.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

With rapid advancements in large language models (LLMs), AI-generated content (AIGC) has emerged as a key driver of technological innovation and economic transformation. Personalizing AIGC services to meet individual user demands is essential but challenging for AIGC service providers (ASPs) due to the subjective and complex demands of mobile users (MUs), as well as the computational and communication resource constraints faced by ASPs. To tackle these challenges, we first develop a novel multi-dimensional quality-of-experience (QoE) metric. This metric comprehensively evaluates AIGC services by integrating accuracy, token count, and timeliness. We focus on a mobile edge computing (MEC)-enabled AIGC network, consisting of multiple ASPs deploying differentiated AIGC models on edge servers and multiple MUs with heterogeneous QoE requirements requesting AIGC services from ASPs. To incentivize ASPs to provide personalized AIGC services under MEC resource constraints, we propose a QoE-driven incentive mechanism. We formulate the problem as an equilibrium problem with equilibrium constraints (EPEC), where MUs as leaders determine rewards, while ASPs as followers optimize resource allocation. To solve this, we develop a dual-perturbation reward optimization algorithm, reducing the implementation complexity of adaptive pricing. Experimental results demonstrate that our proposed mechanism achieves a reduction of approximately $64.9%$ in average computational and communication overhead, while the average service cost for MUs and the resource consumption of ASPs decrease by $66.5%$ and $76.8%$, respectively, compared to state-of-the-art benchmarks.

Problem

• Designing personalized AIGC services under edge resource constraints
• Developing QoE-driven incentive mechanism for service providers
• Reducing computational and communication overhead in edge networks

Innovation

• Multi-dimensional QoE metric integrating accuracy, token, timeliness
• EPEC formulation with dual-perturbation reward optimization algorithm
• QoE-driven incentive mechanism for edge-based AIGC services