Content-Adaptive Memory for Viewer-Aware Energy-Quality Scalable Mobile Video Systems

Mobile devices are becoming ever more popular for streaming videos, which account for the majority of all data traffic on the internet. Memory is a critical component in mobile video processing systems, increasingly dominating power consumption. Today, memory designers are still focusing on hardware-level power optimization techniques, which usually come with significant implementation cost (e.g., silicon area overhead or performance penalty). In this paper, we propose a video content-aware memory technique for power-quality trade-off from viewer's perspectives. Based on the influence of video macroblock characteristics on the viewer's experience, we develop two simple and effective models - decision tree and logistic regression - to enable hardware adaptation. We have also implemented a novel viewer-aware bit-truncation technique which minimizes the impact on the viewer's experience, while introducing energy-quality adaptation to the video storage.