Low Earth orbit (LEO) satellite relays will significantly extend the coverage of mobile networks, enabling users in remote areas to transmit data of real-time events. Nevertheless, the limited power of user devices and the long distance to satellites lead to low signal-to-noise ratio (SNR), which results in high error rates and frequent retransmissions, severely hindering the transmissions of high-dimensional data such as videos. In this paper, we propose a novel method to achieve high error tolerance in satellite-relay video transmissions using generative semantic communications (GSC). For the transmitter, we design and optimize a semantic encoder integrating a pre-trained video encoder with a low-density parity-check (LDPC) encoder, efficiently achieving generalizability and enabling forward error correction. For the receiver, we fine-tune a generative video model using an efficient in-context adaptation algorithm, enabling it to reconstruct videos from error-corrupted semantic information. Simulation results show that our method achieves 2.5 dB higher video peak SNR than conventional semantic communications at an error rate of 45%, and remains robust when the error rate exceeds 80%.
@article{arxiv.2604.25184,
title = {Enabling High Error Tolerance in Satellite Video Transmissions by Generative Semantic Communication},
author = {Zixin Zhao and Jingzhi Hu and Geoffrey Ye Li},
journal= {arXiv preprint arXiv:2604.25184},
year = {2026}
}