SR-ABR: Super Resolution Integrated ABR Algorithm for Cloud-Based Video Streaming

Abstract

Super-resolution is a promising solution to improve the quality of experience (QoE) for cloud-based video streaming when the network resources between clients and the cloud vendors become scarce. Specifically, the received video can be enhanced with a trained super-resolution model running on the client-side. However, all the existing solutions ignore the content-induced performance variability of Super-Resolution Deep Neural Network (SR-DNN) models, which means the same super-resolution models have different enhancement effects on the different parts of videos because of video content variation. That leads to unreasonable bitrate selection, resulting in low video QoE, e.g., low bitrate, rebuffering, or video quality jitters. Thus, in this paper, we propose SR-ABR, a super-resolution integrated adaptive bitrate (ABR) algorithm, which considers the content-induced performance variability of SR-DNNs into the bitrate decision process. Due to complex network conditions and video content, SR-ABR adopts deep reinforcement learning (DRL) to select future bitrate for adapting to a wide range of environments. Moreover, to utilize the content-induced performance variability of SR-DNNs efficiently, we first define the performance variability of SR-DNNs over different video content, and then use a 2D convolution kernel to distill the features of the performance variability of the SR-DNNs to a short future video segment (several chunks) as part of the inputs. We compare SR-ABR with the related state-of-the-art works using trace-driven simulation under various real-world traces. The experiments show that SR-ABR outperforms the best state-of-the-art work NAS with the gain in average QoE of 4.3%–46.2% and 18.9%–42.1% under FCC and 3G/HSDPA network traces, respectively.