  Adaptive bitrate (ABR) using conventional codecs cannot further modify the
bitrate once a decision has been made, exhibiting limited adaptation
capability. This may result in either overly conservative or overly aggressive
bitrate selection, which could cause either inefficient utilization of the
network bandwidth or frequent re-buffering, respectively. Neural representation
for video (NeRV), which embeds the video content into neural network weights,
allows video reconstruction with incomplete models. Specifically, the recovery
of one frame can be achieved without relying on the decoding of adjacent
frames. NeRV has the potential to provide high video reconstruction quality
and, more importantly, pave the way for developing more flexible ABR strategies
for video transmission. In this work, a new framework, named Evolutional Video
streaming Adaptation via Neural representation (EVAN), which can adaptively
transmit NeRV models based on soft actor-critic (SAC) reinforcement learning,
is proposed. EVAN is trained with a more exploitative strategy and utilizes
progressive playback to avoid re-buffering. Experiments showed that EVAN can
outperform existing ABRs with 50% reduction in re-buffering and achieve nearly
20% .
