Batch Denoising for AIGC Service Provisioning in Wireless Edge Networks

TL;DR

This paper introduces a batch denoising framework and an optimization algorithm for AIGC service provisioning in wireless edge networks, significantly improving quality and reducing latency through joint content generation and transmission optimization.

Contribution

It proposes a novel batch denoising method and the STACKING algorithm for joint optimization, enhancing AIGC service quality and efficiency in wireless edge networks.

Findings

Batch denoising reduces per-step delay and enhances parallelism.

STACKING algorithm optimizes denoising without specific quality function dependence.

Simulation shows improved service quality and lower latency.

Abstract

Artificial intelligence-generated content (AIGC) service provisioning in wireless edge networks involves two phases: content generation on edge servers and content transmission to mobile devices. In this paper, we take image generation as a representative application and propose a batch denoising framework, followed by a joint optimization of content generation and transmission, with the objective of maximizing the average AIGC service quality under an end-to-end service delay constraint. Motivated by the empirical observations that (i) batch denoising effectively reduces per-step denoising delay by enhancing parallelism and (ii) early denoising steps have a greater impact on generation quality than later steps, we develop the STACKING algorithm to optimize batch denoising. The STACKING operates independently of any specific form of the content quality function and achieves lower computational complexity. Building on the batch solution, we further optimize bandwidth allocation across AIGC services. Simulation results demonstrate the superior performance of our algorithm in delivering high-quality, lower-latency AIGC services.