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 $10^{-2}$.

基于实时深度学习的 H.264 控制器通过动态估算最优的编码器参数来维持视频质量，并在最小化压缩视频的平均比特率的同时实现高达 2.5 倍的带宽使用改善，且非符合概率低于 10^-2。

基于深度学习的实时标准视频压缩质量控制技术在直播中的应用

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

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.

提出了一种实时基于深度学习的 H.264 控制器，利用即时的信道质量数据和视频块动态估计最优的编码器参数，以保持编码视频的比特率略低于可用信道比特率，验证了该方法在视频质量上相较于最先进的自适应比特率视频流媒体有 10-20 dB 的改进，平均数据包丢失率低至 0.002。