Joint Optimization of Inference and Consensus for Blockchain-Enhanced Edge AIGC Services

Abstract

The rapid advancement of generative artificial intelligence (GAI) has led to the widespread adoption of AI-generated content (AIGC), which generally leverages generative diffusion models (GDMs) to create high-quality multimodal content. By integrating mobile edge computing (MEC) and model compression techniques, edge AIGC can provide low-latency services for mobile users (MUs) at the network edge, thereby unlocking greater potential. However, such a paradigm is vulnerable to critical security risks, such as tampering and plagiarism attacks on AIGC products. To protect the copyright of AIGC products, blockchain technology has been introduced as a promising solution. Nevertheless, most existing studies on the integrated blockchain-edge AIGC framework focus primarily on management mechanisms, while neglecting the inherent resource allocation problem for the tightly coupled AIGC inference and blockchain consensus processes, which severely limits the overall system efficiency. In this work, we propose a blockchain-enhanced edge AIGC service framework over MEC networks, where multiple edge servers (ESs) act as both model hosts generating contents for MUs and miners participating in blockchain consensus. In such a framework, we study the joint optimization of AIGC inference and blockchain consensus, including MU association, inference steps, as well as communication and computation resource allocation, aiming to maximize the total system utility. To tackle the challenges posed by mixed discrete and continuous optimization variables, we decompose the orginal problem into four sequential subproblems and propose a Joint MU association, Inference step allocation, Bandwidth allocation, Power allocation, and Streaming multiprocessor allocation (JMIBPS) algorithm based on Markov approximation, interior point method, and successive convex approximation. Simulation results validate the effectiveness of the proposed algorithm, which improves the total system utility by at least 10.21% compared with representative benchmark schemes.