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.

翻译：本文提出了一种用于全景视频传输的跨层加密语义通信（CLESC）框架，该框架融合了特征提取、编码、加密、循环冗余校验（CRC）和重传过程，以实现语义通信与传统通信系统的兼容性。此外，我们提出了一种自适应跨层传输机制，该机制能够根据语义信息的重要性动态调整CRC、信道编码和重传方案。这确保了在恶劣传输条件下重要信息能够被优先处理。为验证上述框架，我们还设计了一个端到端的自适应全景视频语义传输（APVST）网络，该网络利用深度联合信源信道编码（Deep JSCC）结构和注意力机制，并结合纬度自适应模块，以实现全景视频的自适应语义特征提取和变长编码。所提出的CLESC框架同样适用于其他模态数据的传输。仿真结果表明，所提出的CLESC框架有效实现了语义通信与传统通信系统之间的兼容与自适应，提升了传输效率和信道适应性。与传统跨层传输方案相比，CLESC框架能够降低85%的带宽消耗，并在低信噪比（SNR）条件下展现出显著优势。