  Ensuring high-quality video content for wireless users has become
increasingly vital. Nevertheless, maintaining a consistent level of video
quality faces challenges due to the fluctuating encoded bitrate, primarily
caused by dynamic video content, especially in live streaming scenarios. Video
compression is typically employed to eliminate unnecessary redundancies within
and between video frames, thereby reducing the required bandwidth for video
transmission. The encoded bitrate and the quality of the compressed video
depend on encoder parameters, specifically, the quantization parameter (QP).
Poor choices of encoder parameters can result in reduced bandwidth efficiency
and high likelihood of non-conformance. Non-conformance refers to the violation
of the peak signal-to-noise ratio (PSNR) constraint for an encoded video
segment. To address these issues, a real-time deep learning-based H.264
controller is proposed. This controller dynamically estimates the optimal
encoder parameters based on the content of a video chunk with minimal delay.
The objective is to maintain video quality in terms of PSNR above a specified
threshold while minimizing the average bitrate of the compressed video.
Experimental results, conducted on both QCIF dataset and a diverse range of
random videos from public datasets, validate the effectiveness of this
approach. Notably, it achieves improvements of up to 2.5 times in average
bandwidth usage compared to the state-of-the-art adaptive bitrate video
streaming, with a negligible non-conformance probability below $10^{-2}$.
