  Deep learning has shown impressive performance in semantic segmentation, but
it is still unaffordable for resource-constrained mobile devices. While
offloading computation tasks is promising, the high traffic demands overwhelm
the limited bandwidth. Existing compression algorithms are not fit for semantic
segmentation, as the lack of obvious and concentrated regions of interest
(RoIs) forces the adoption of uniform compression strategies, leading to low
compression ratios or accuracy. This paper introduces STAC, a DNN-driven
compression scheme tailored for edge-assisted semantic video segmentation. STAC
is the first to exploit DNN's gradients as spatial sensitivity metrics for
spatial adaptive compression and achieves superior compression ratio and
accuracy. Yet, it is challenging to adapt this content-customized compression
to videos. Practical issues include varying spatial sensitivity and huge
bandwidth consumption for compression strategy feedback and offloading. We
tackle these issues through a spatiotemporal adaptive scheme, which (1) takes
partial strategy generation operations offline to reduce communication load,
and (2) propagates compression strategies and segmentation results across
frames through dense optical flow, and adaptively offloads keyframes to
accommodate video content. We implement STAC on a commodity mobile device.
Experiments show that STAC can save up to 20.95% of bandwidth without losing
accuracy, compared to the state-of-the-art algorithm.
