With the advent of the 6G era, the efficient transmission of large-scale, high-resolution videos poses a significant challenge to existing communication systems. Compared to traditional video communication systems, which are becoming increasingly complex and difficult to optimize, semantic communication offers new possibilities and flexibility for optimization, effectively resisting noise interference with low-design-complexity joint coding. In this paper, we propose an efficient semantic-channel joint coding model for wireless video transmission, which adjusts the rate output based on the contextual information of video sequences and channel conditions, ensuring the reliability of video transmission even under different channel conditions. Across standard video test sequences under different scenarios, experiments show that under reliable channel state conditions, our scheme can save 22% to 56% of bandwidth compared to traditional video wireless transmission systems composed of H.265, 5G LDPC, and digital modulation, while also overcoming the cliff effect and achieving efficient video transmission under harsh channel conditions. Our scheme provides strong technical support for the practical application of semantic communication.
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Huang et al. (2024) studied this question.
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