Deep Learning-Based Real-Time Rate Control for Live Streaming on
  Wireless Networks
Matin Mortaheb Mohammad A. Amir Khojastepour Srimat T. Chakradhar Sennur Ulukus
Providing wireless users with high-quality video content has become
increasingly important. However, ensuring consistent video quality poses
challenges due to variable encoded bitrate caused by dynamic video content and
fluctuating channel bitrate caused by wireless fading effects. Suboptimal
selection of encoder parameters can lead to video quality loss due to
underutilized bandwidth or the introduction of video artifacts due to packet
loss. To address this, a real-time deep learning based H.264 controller is
proposed. This controller leverages instantaneous channel quality data driven
from the physical layer, along with the video chunk, to dynamically estimate
the optimal encoder parameters with a negligible delay in real-time. The
objective is to maintain an encoded video bitrate slightly below the available
channel bitrate. Experimental results, conducted on both QCIF dataset and a
diverse selection of random videos from public datasets, validate the
effectiveness of the approach. Remarkably, improvements of 10-20 dB in PSNR
with repect to the state-of-the-art adaptive bitrate video streaming is
achieved, with an average packet drop rate as low as 0.002.