Token-Aware QoE Optimization for Multimodal AIGC Inference Tasks in Space-Air-Ground Integrated Networks
IEEE Transactions on Mobile Computing, pp. 1–18, 2026
- +31.9%
- Per-user QoE over TD3
- +69.2%
- Per-user QoE over Greedy
Abstract
The space-air-ground integrated network (SAGIN) can provide ubiquitous multimodal artificial intelligence-generated content (AIGC) services. In SAGIN, lightweight task-specific models hosted on uncrewed aerial vehicles (UAVs) provide low-latency inference, while full-scale foundation models deployed on low Earth orbit (LEO) satellites provide high-quality inference. However, efficient AIGC inference in SAGIN is hindered by the tight coupling between computation and cross-tier transmission under multi-timescale network dynamics. In this paper, we maximize the long-term per-user quality of experience (QoE), which captures both the generation quality and the end-to-end streaming latency of heterogeneous multimodal AIGC requests. To this end, we develop a unified token-level latency model that characterizes the time-to-first-token (TTFT) and the time-per-output-token (TPOT) along multi-tier offloading paths. We then formulate the joint optimization of model caching, UAV deployment, inference offloading, semantic compression, and resource allocation as a two-timescale mixed-integer nonlinear program (MINLP). For the slow-timescale subproblem, a knowledge-driven planner generates and evaluates candidate UAV deployment and model caching configurations from historical demand priors. For the fast-timescale subproblem, we propose a conditional diffusion twin delayed deep deterministic policy gradient (CD-TD3) algorithm with a hybrid action space, which optimizes offloading decisions, compression ratios, and resource allocation in real time. Simulation results show that the proposed framework improves the per-user QoE by 31.9% over TD3 and by 69.2% over the Greedy heuristic.
Cite
Linghao Zhang, Bo Xu, Haitao Zhao, Jinlong Sun, Jun Xiong, Xianbin Wang, “Token-Aware QoE Optimization for Multimodal AIGC Inference Tasks in Space-Air-Ground Integrated Networks”, IEEE Transactions on Mobile Computing, pp. 1–18, 2026, doi: 10.1109/TMC.2026.3743972.
@article{zhang2026tokenaware,
author = {Zhang, Linghao and Xu, Bo and Zhao, Haitao and Sun, Jinlong and Xiong, Jun and Wang, Xianbin},
title = {Token-Aware QoE Optimization for Multimodal AIGC Inference Tasks in Space-Air-Ground Integrated Networks},
journal = {IEEE Transactions on Mobile Computing},
year = {2026},
pages = {1--18},
doi = {10.1109/TMC.2026.3743972},
note = {Early Access},
}