标题： 基于深度学习的无线网络实时流媒体速率控制 显示英文标题

标题： Deep Learning-Based Real-Time Rate Control for Live Streaming on Wireless Networks

摘要： 提供高质量的视频内容给无线用户变得越来越重要。然而，由于动态视频内容导致的编码比特率变化和无线衰落效应引起的信道比特率波动，确保视频质量的一致性面临着挑战。次优的编码器参数选择可能导致由于带宽利用不足或数据包丢失引入的视频失真而造成视频质量损失。为了解决这个问题，提出了一种基于实时深度学习的H.264控制器。该控制器利用来自物理层的即时信道质量数据以及视频片段，动态估计最优编码参数，并且在实时操作中有微小延迟。目标是使编码视频比特率略低于可用的信道比特率。实验结果，在QCIF数据集以及从公共数据集中随机选择的各种视频上进行，验证了该方法的有效性。值得注意的是，与最先进的自适应比特率视频流相比，峰值信噪比提高了10-20 dB，平均数据包丢失率低至0.002。 显示英文摘要

摘要： 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.