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 $10^{-2}$.

翻译：确保无线用户获得高质量视频内容已变得日益关键。然而，由于动态视频内容（尤其在直播场景中）导致编码比特率波动，维持稳定的视频质量面临挑战。视频压缩通常用于消除视频帧内和帧间的冗余信息，从而降低视频传输所需的带宽。编码比特率和压缩视频质量取决于编码器参数，特别是量化参数（QP）。编码器参数选择不当可能导致带宽效率降低并增加不符合规范的概率。不符合规范指编码视频片段违反峰值信噪比（PSNR）约束。为解决这些问题，提出了一种基于深度学习的实时H.264控制器。该控制器根据视频块内容以最小延迟动态估计最优编码器参数，目标是在将压缩视频的平均比特率最小化的同时，确保视频质量（以PSNR衡量）保持在指定阈值以上。在QCIF数据集以及来自公开数据集的多样化随机视频上进行的实验结果验证了该方法的有效性。值得注意的是，与当前最先进的自适应比特率视频流相比，该方法在平均带宽使用上实现了高达2.5倍的提升，且不符合规范概率低于$10^{-2}$。