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

Publication Date: 5/8/2024

Event: IEEE International Conference on Machine Learning for Communication and Networking (IEEE ICMLCN 2024)

Reference: pp. 312-316

Authors: Mohammad A. Khojastepour, NEC Laboratories America, Inc.; Matin Mortaheb, NEC Laboratories America, Inc., University of Maryland, College Park; Srimat T. Chakradhar, NEC Laboratories America, Inc.; Sennur Ulukus, University of Maryland, College Park

Abstract: Providing wireless users with high-quality video content has become increasingly important. However, ensuring consistent video quality poses challenges due to variable encodedbitrate 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 respect to the state-of-the art adaptive bitrate video streaming is achieved, with an average packet drop rate as low as 0.002.

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