Batch Denoising for AIGC Service Provisioning in Wireless Edge Networks

2025-11-24

AI Summary

To address the trade-off between end-to-end latency constraints and generation quality in AIGC services deployed over wireless edge networks, this paper proposes a batched denoising co-optimization framework. Methodologically: (1) a batched denoising mechanism is introduced to enhance parallelism across denoising steps, thereby reducing per-step latency; (2) a lightweight STACKING algorithm is designed to dynamically allocate computational resources across denoising stages based on their differential contributions to final output quality—without requiring explicit quality metrics and with low computational complexity; (3) generation scheduling and bandwidth allocation are jointly optimized to enable deep coordination between inference and transmission. Experiments demonstrate that, under stringent latency constraints, the framework significantly improves average AIGC service quality: generation quality increases by up to 23.6%, while end-to-end latency decreases by 31.4%.

Technology Category

• Search and Optimization: Algorithm Configuration
• Planning, Routing, and Scheduling: Planning with Language Models
• Natural Language Processing: Generation

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Agentic search

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.

Problem

• Optimizing batch denoising to reduce AIGC service latency
• Jointly managing content generation and transmission in edge networks
• Maximizing AIGC quality under end-to-end delay constraints

Innovation

• Batch denoising framework reduces per-step delay
• STACKING algorithm optimizes batch denoising independently
• Joint optimization of content generation and transmission