Deep Learning-Based Real-Time Rate Control for Live Streaming on Wireless Networks
This addresses the challenge of variable video quality for wireless users in live streaming, representing a strong specific gain rather than a foundational advancement.
The paper tackles the problem of maintaining consistent video quality in live streaming over wireless networks by proposing a real-time deep learning controller that dynamically adjusts encoder parameters based on channel conditions and video content, achieving improvements of 10-20 dB in PSNR and an average packet drop rate as low as 0.002 compared to state-of-the-art methods.
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.