  Low Earth Orbit (LEO) satellite communication is a crucial component of
future 6G communication networks, contributing to the development of an
integrated satellite-terrestrial network. In the forthcoming
satellite-to-ground network, the idle computational resources of LEO satellites
can serve as edge servers, delivering intelligent task computation services to
ground users. Existing research on satellite-to-ground computation primarily
focuses on designing efficient task scheduling algorithms to provide
straightforward computation services to ground users. This study aims to
integrate satellite edge networks with Artificial Intelligence-Generated
Content (AIGC) technology to offer personalized AIGC services to ground users,
such as customized digital watermarking services. Firstly, we propose a
satellite-to-ground edge network architecture, enabling bidirectional
communication between visible LEO satellites and ground users. Each LEO
satellite is equipped with intelligent algorithms supporting various
AIGC-assisted digital watermarking technologies with different precision
levels. Secondly, considering metrics like satellite visibility,
satellite-to-ground communication stability, digital watermark quality,
satellite-to-ground communication time, digital watermarking time, and ground
user energy consumption, we construct an AIGC-assisted digital watermarking
model based on the satellite-to-ground edge network. Finally, we introduce a
reinforcement learning-based task scheduling algorithm to obtain an optimal
strategy. Experimental results demonstrate that our approach effectively meets
the watermark generation needs of ground users, achieving a well-balanced
trade-off between generation time and user energy consumption. We anticipate
that this work will provide an effective solution for the intelligent services
in satellite-to-ground edge networks.
