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Providing wireless users with high-quality video content has becomeincreasingly important. However, ensuring consistent video quality poseschallenges due to variable encoded bitrate caused by dynamic video content andfluctuating channel bitrate caused by wireless fading effects. Suboptimalselection of encoder parameters can lead to video quality loss due tounderutilized bandwidth or the introduction of video artifacts due to packetloss. To address this, a real-time deep learning based H.264 controller isproposed. This controller leverages instantaneous channel quality data drivenfrom the physical layer, along with the video chunk, to dynamically estimatethe optimal encoder parameters with a negligible delay in real-time. Theobjective is to maintain an encoded video bitrate slightly below the availablechannel bitrate. Experimental results, conducted on both QCIF dataset and adiverse selection of random videos from public datasets, validate theeffectiveness of the approach. Remarkably, improvements of 10-20 dB in PSNRwith repect to the state-of-the-art adaptive bitrate video streaming isachieved, with an average packet drop rate as low as 0.002.

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