  This paper studies an end-to-end video semantic communication system for
massive communication. In the considered system, the transmitter must
continuously send the video to the receiver to facilitate character
reconstruction in immersive applications, such as interactive video conference.
However, transmitting the original video information with substantial amounts
of data poses a challenge to the limited wireless resources. To address this
issue, we reduce the amount of data transmitted by making the transmitter
extract and send the semantic information from the video, which refines the
major object and the correlation of time and space in the video. Specifically,
we first develop a video semantic communication system based on major object
extraction (MOE) and contextual video encoding (CVE) to achieve efficient video
transmission. Then, we design the MOE and CVE modules with convolutional neural
network based motion estimation, contextual extraction and entropy coding.
Simulation results show that compared to the traditional coding schemes, the
proposed method can reduce the amount of transmitted data by up to 25% while
increasing the peak signal-to-noise ratio (PSNR) of the reconstructed video by
up to 14%.
