Semantic Video Transmission Over LEO Satellites With Graph Neural Networks

Providing large-scale video surveillance in remote areas is challenging due to limited terrestrial communication infrastructure and the high data rates required by raw video transmission. Low Earth Orbit (LEO) satellites offer a viable connectivity solution, but their uplink capacity remains constrained, particularly for low-power devices. Semantic communication addresses this limitation by transmitting task-relevant information rather than raw video streams. In this work, we address the semantic information transmission problem in camera-to-satellite uplink communication. The task consists of selecting semantic representations from all cameras to maximize the total transmitted semantic information under strict resource constraints. We formulate the problem as an NP-hard constrained optimization task and address it using a low-complexity and scalable graph node classification approach that recasts representation selection. We propose a lightweight graph neural network that exploits the camera–satellite topology to predict the optimal semantic representation for each camera by learning context-aware node embeddings through message passing. A search-based post-processing algorithm is introduced to enforce the feasibility of the predicted solutions within transmission time requirements. Our extensive evaluations show that the proposed approach outperforms learning-based and search-based baselines in terms of accuracy, optimality, and constraint satisfaction, while maintaining low inference latency. The proposed system scales to large camera networks and enables semantic information transmission with minimal information loss and an average gap of 0.67% compared to the solver-based reference solution.