Enabling High Error Tolerance in Satellite Video Transmissions by Generative Semantic Communication

TL;DR

This paper introduces a generative semantic communication framework for satellite video transmission that significantly improves error tolerance and video quality under low SNR conditions.

Contribution

It presents a novel semantic encoding and decoding approach combining pre-trained video encoders, LDPC codes, and generative models with in-context adaptation.

Findings

Achieves 2.5 dB higher video peak SNR at 45% error rate.

Remains robust with error rates exceeding 80%.

Enhances error tolerance in satellite video communications.

Abstract

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%.