SST-V:A Scalable Semantic Transmission Framework for Video

The emerging new services in the sixth generation(6G)communication system impose increasingly stringent requirements and challenges on video transmission.Semantic communications are envisioned as a promising solution to these challenges.This paper pro-vides a highly-efficient solution to video transmission by proposing a scalable semantic transmission algorithm,named scalable semantic transmission framework for video(SST-V),which jointly considers the semantic importance and channel conditions.Specifically,a seman-tic importance evaluation module is designed to extract more informative semantic features according to the estimated importance level,fa-cilitating high-efficiency semantic coding.By further considering the channel condition,a cascaded learning based scalable joint semantic-channel coding algorithm is proposed,which autonomously adapts the semantic coding and channel coding strategies to the specific signal-to-noise ratio(SNR).Simulation results show that SST-V achieves better video reconstruction performance,while significantly reducing the transmission overhead.