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Ensuring high-quality video content for wireless users has becomeincreasingly vital. Nevertheless, maintaining a consistent level of videoquality faces challenges due to the fluctuating encoded bitrate, primarilycaused by dynamic video content, especially in live streaming scenarios. Videocompression is typically employed to eliminate unnecessary redundancies withinand between video frames, thereby reducing the required bandwidth for videotransmission. The encoded bitrate and the quality of the compressed videodepend on encoder parameters, specifically, the quantization parameter (QP).Poor choices of encoder parameters can result in reduced bandwidth efficiencyand high likelihood of non-conformance. Non-conformance refers to the violationof the peak signal-to-noise ratio (PSNR) constraint for an encoded videosegment. To address these issues, a real-time deep learning-based H.264controller is proposed. This controller dynamically estimates the optimalencoder 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 specifiedthreshold while minimizing the average bitrate of the compressed video.Experimental results, conducted on both QCIF dataset and a diverse range ofrandom videos from public datasets, validate the effectiveness of thisapproach. Notably, it achieves improvements of up to 2.5 times in averagebandwidth usage compared to the state-of-the-art adaptive bitrate videostreaming, with a negligible non-conformance probability below $10^{-2}$.

Deep Learning-Based Real-Time Quality Control of Standard Video Compression for Live Streaming