  We present DeepWiVe, the first-ever end-to-end joint source-channel coding
(JSCC) video transmission scheme that leverages the power of deep neural
networks (DNNs) to directly map video signals to channel symbols, combining
video compression, channel coding, and modulation steps into a single neural
transform. Our DNN decoder predicts residuals without distortion feedback,
which improves video quality by accounting for occlusion/disocclusion and
camera movements. We simultaneously train different bandwidth allocation
networks for the frames to allow variable bandwidth transmission. Then, we
train a bandwidth allocation network using reinforcement learning (RL) that
optimizes the allocation of limited available channel bandwidth among video
frames to maximize overall visual quality. Our results show that DeepWiVe can
overcome the cliff-effect, which is prevalent in conventional separation-based
digital communication schemes, and achieve graceful degradation with the
mismatch between the estimated and actual channel qualities. DeepWiVe
outperforms H.264 video compression followed by low-density parity check (LDPC)
codes in all channel conditions by up to 0.0462 on average in terms of the
multi-scale structural similarity index measure (MS-SSIM), while beating H.265
+ LDPC by up to 0.0058 on average. We also illustrate the importance of
optimizing bandwidth allocation in JSCC video transmission by showing that our
optimal bandwidth allocation policy is superior to the na\"ive uniform
allocation. We believe this is an important step towards fulfilling the
potential of an end-to-end optimized JSCC wireless video transmission system
that is superior to the current separation-based designs.
