Semantic communication is emerging as a promising paradigm for the future of wireless communication, with recent progress in deep learning-based joint source-channel coding (JSCC) achieving notable success. However, the performance of wireless communication systems is often constrained by the variability and dynamics of channel conditions, which presents significant challenges for system robustness and adaptability. In real-world applications, such as in railway environments where multiple diverse scenarios are encountered, including rural, viaduct, tunnel and hilly terrain scenarios, the variability of the channel conditions can be particularly pronounced. Existing deep learning-based JSCC approaches typically train models in fixed channel models, limiting their ability to generalize across different channel scenarios. As a result, a model trained for one specific channel type is ineffective in others, necessitating the deployment of distinct models for different channel models. To address this issue, we propose a novel channel scenario adaptive deep joint source-channel coding (SA-DJSCC) method that integrates the channel type label into both the training and inference phases. By incorporating channel scenario information into the JSCC model, our approach allows a single model to effectively generalize across multiple channel models. Experimental results show that our method improves scenario adaptability and outperforms existing approaches across different channel models and signal-to-noise ratio conditions. This work represents a key advancement towards developing more resilient and efficient semantic communication systems capable of operating in dynamic wireless environments.

Although semantic communication (SC) has shown its potential in efficiently transmitting multimodal data such as texts, speeches and images, SC for videos has focused primarily on pixel-level reconstruction. However, these SC systems may be suboptimal for downstream intelligent tasks. Moreover, SC systems without pixel-level video reconstruction present advantages by achieving higher bandwidth efficiency and real-time performance of various intelligent tasks. The difficulty in such system design lies in the extraction of task-related compact semantic representations and their accurate delivery over noisy channels. In this paper, we propose an end-to-end SC system, named VideoQA-SC for video question answering (VideoQA) tasks. Our goal is to accomplish VideoQA tasks directly based on video semantics over noisy or fading wireless channels, bypassing the need for video reconstruction at the receiver. To this end, we develop a spatiotemporal semantic encoder for effective video semantic extraction, and a learning-based bandwidth-adaptive deep joint source-channel coding (DJSCC) scheme for efficient and robust video semantic transmission. Experiments demonstrate that VideoQA-SC outperforms traditional and advanced DJSCC-based SC systems that rely on video reconstruction at the receiver under a wide range of channel conditions and bandwidth constraints. In particular, when the signal-to-noise ratio is low, VideoQA-SC can improve the answer accuracy by 5.17% while saving almost 99.5% of the bandwidth at the same time, compared with the advanced DJSCC-based SC system. Our results show the great potential of SC system design for video applications.