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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 the data traffic on the Internet. Memory is a critical component in mobile video processing systems, increasingly dominating the 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 tradeoff 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.