Super-Resolution Based Bitrate Adaptation for HTTP Adaptive Streaming for Mobile Devices

Abstract

The advancement of hardware capabilities in recent years made it possible to apply deep neural network (DNN) based approaches on mobile devices. This paper introduces a lightweight super-resolution (SR) network, namely SR-ABR Net, deployed at mobile devices to upgrade low-resolution/low-quality videos and a novel adaptive bitrate (ABR) algorithm, namely WISH-SR, that leverages SR networks at the client to improve the video quality depending on the client's context. WISH-SR takes into account mobile device properties, video characteristics, and user preferences. Experimental results show that the proposed SR-ABR Net can improve the video quality compared to traditional SR approaches while running in real time. Moreover, the proposed WISH-SR can significantly boost the visual quality of the delivered content while reducing both bandwidth consumption and number of stalling events.