  The enormous data volume of video poses a significant burden on the network.
Particularly, transferring high-definition surveillance videos to the cloud
consumes a significant amount of spectrum resources. To address these issues,
we propose a surveillance video transmission system enabled by end-cloud
computing. Specifically, the cameras actively down-sample the original video
and then a redundant frame elimination module is employed to further reduce the
data volume of surveillance videos. Then we develop a key-frame assisted video
super-resolution model to reconstruct the high-quality video at the cloud side.
Moreover, we propose a strategy of extracting key frames from source videos for
better reconstruction performance by utilizing the peak signal-to-noise ratio
(PSNR) of adjacent frames to measure the propagation distance of key frame
information. Simulation results show that the developed system can effectively
reduce the data volume by the end-cloud collaboration and outperforms existing
video super-resolution models significantly in terms of PSNR and structural
similarity index (SSIM).
