Exploiting media stream similarity for energy-efficient decoding and resource prediction

Abstract

This article introduces a novel approach to energy-efficient media stream decoding that is based on the notion of media stream similarity. The key idea is that platform-independent scenarios with similar decoding complexity can be identified within and across media streams. A device that decodes a media stream annotated with scenario information can then adjust its processor clock frequency and voltage level based on these scenarios for lower energy consumption. Our evaluation, done using the H.264 AVC decoder and 12 reference video streams, shows an average energy reduction of 44% while missing less than 0.2% of the frame deadlines using scenario-driven video decoding. An additional application of scenario-based media stream annotation is to predict required resources (compute power and energy) for consuming a given service on a given device. Resource prediction is extremely useful in a client-server setup in which the client requests a media service from the server or content provider. The content provider (in cooperation with the client) can then determine what service quality to deliver, given the client's available resources. Scenario-aware resource prediction can predict (compute power and energy) consumption with errors less than 4% (and an overall average 1.4% error).