  In this paper, we design a new class of high-efficiency deep joint
source-channel coding methods to achieve end-to-end video transmission over
wireless channels. The proposed methods exploit nonlinear transform and
conditional coding architecture to adaptively extract semantic features across
video frames, and transmit semantic feature domain representations over
wireless channels via deep joint source-channel coding. Our framework is
collected under the name deep video semantic transmission (DVST). In
particular, benefiting from the strong temporal prior provided by the feature
domain context, the learned nonlinear transform function becomes temporally
adaptive, resulting in a richer and more accurate entropy model guiding the
transmission of current frame. Accordingly, a novel rate adaptive transmission
mechanism is developed to customize deep joint source-channel coding for video
sources. It learns to allocate the limited channel bandwidth within and among
video frames to maximize the overall transmission performance. The whole DVST
design is formulated as an optimization problem whose goal is to minimize the
end-to-end transmission rate-distortion performance under perceptual quality
metrics or machine vision task performance metrics. Across standard video
source test sequences and various communication scenarios, experiments show
that our DVST can generally surpass traditional wireless video coded
transmission schemes. The proposed DVST framework can well support future
semantic communications due to its video content-aware and machine vision task
integration abilities.
