Neural Adaptive Contextual Video Streaming

Abstract

Video streaming services typically employ traditional codecs, such as H.264, to encode videos into multiple bitrate representations. These codecs are tightly limited by discrete quantization parameters (QPs), resulting in encoded rates that do not align with the target bitrate. Additionally, the subpar video quality produced by conventional codecs does not meet the demands of high-resolution communication. Considering the limitations of traditional codecs, we take a fresh new approach to video streaming by leveraging advanced deep learning-based video codecs. Specifically, we develop a neural adaptive contextual video streaming framework that incorporates: 1) an ensemble deep reinforcement learning based adaptive bitrate algorithm named TSAC that enables continuous bitrate adjustment to varying network conditions 2) a two-stage proportional-integral-derivative-based rate control module that dynamically fine-tunes QPs to ensure the encoded bitrate aligning with the target bitrate. Furthermore, we implement intra-GoP and inter-GoP techniques to accelerate the inference process of the contextual video codec for real-time processing needs. Our experiments demonstrate that the average relative error in bitrate remains below 2%, the quality of experience provided by our TSAC agents surpasses that of existing discrete algorithms by 13%-20%. Our optimization techniques enable real-time decoding at approximately 24 frames per second for quad high definition videos.