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Cross-Layer Encrypted Semantic Communication Framework for Panoramic Video Transmission

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Název: Cross-Layer Encrypted Semantic Communication Framework for Panoramic Video Transmission
Autoři: Haixiao Gao, Mengying Sun, Xiaodong Xu, Bingxuan Xu, Shujun Han, Bizhu Wang, Sheng Jiang, Chen Dong, Ping Zhang
Zdroj: IEEE Internet of Things Journal. 12:40545-40562
Publication Status: Preprint
Informace o vydavateli: Institute of Electrical and Electronics Engineers (IEEE), 2025.
Rok vydání: 2025
Témata: FOS: Computer and information sciences, Computer Science - Cryptography and Security, Image and Video Processing (eess.IV), FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Image and Video Processing, Cryptography and Security (cs.CR), Computer Science - Multimedia, Multimedia (cs.MM)
Popis: In this paper, we propose a cross-layer encrypted semantic communication (CLESC) framework for panoramic video transmission, incorporating feature extraction, encoding, encryption, cyclic redundancy check (CRC), and retransmission processes to achieve compatibility between semantic communication and traditional communication systems. Additionally, we propose an adaptive cross-layer transmission mechanism that dynamically adjusts CRC, channel coding, and retransmission schemes based on the importance of semantic information. This ensures that important information is prioritized under poor transmission conditions. To verify the aforementioned framework, we also design an end-to-end adaptive panoramic video semantic transmission (APVST) network that leverages a deep joint source-channel coding (Deep JSCC) structure and attention mechanism, integrated with a latitude adaptive module that facilitates adaptive semantic feature extraction and variable-length encoding of panoramic videos. The proposed CLESC is also applicable to the transmission of other modal data. Simulation results demonstrate that the proposed CLESC effectively achieves compatibility and adaptation between semantic communication and traditional communication systems, improving both transmission efficiency and channel adaptability. Compared to traditional cross-layer transmission schemes, the CLESC framework can reduce bandwidth consumption by 85% while showing significant advantages under low signal-to-noise ratio (SNR) conditions.
Druh dokumentu: Article
ISSN: 2372-2541
DOI: 10.1109/jiot.2025.3592181
DOI: 10.48550/arxiv.2411.12776
Přístupová URL adresa: http://arxiv.org/abs/2411.12776
Rights: IEEE Copyright
arXiv Non-Exclusive Distribution
Přístupové číslo: edsair.doi.dedup.....a25486e07eb64016deb721251f0b1366
Databáze: OpenAIRE
Popis
Abstrakt:In this paper, we propose a cross-layer encrypted semantic communication (CLESC) framework for panoramic video transmission, incorporating feature extraction, encoding, encryption, cyclic redundancy check (CRC), and retransmission processes to achieve compatibility between semantic communication and traditional communication systems. Additionally, we propose an adaptive cross-layer transmission mechanism that dynamically adjusts CRC, channel coding, and retransmission schemes based on the importance of semantic information. This ensures that important information is prioritized under poor transmission conditions. To verify the aforementioned framework, we also design an end-to-end adaptive panoramic video semantic transmission (APVST) network that leverages a deep joint source-channel coding (Deep JSCC) structure and attention mechanism, integrated with a latitude adaptive module that facilitates adaptive semantic feature extraction and variable-length encoding of panoramic videos. The proposed CLESC is also applicable to the transmission of other modal data. Simulation results demonstrate that the proposed CLESC effectively achieves compatibility and adaptation between semantic communication and traditional communication systems, improving both transmission efficiency and channel adaptability. Compared to traditional cross-layer transmission schemes, the CLESC framework can reduce bandwidth consumption by 85% while showing significant advantages under low signal-to-noise ratio (SNR) conditions.
ISSN:23722541
DOI:10.1109/jiot.2025.3592181