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

Area of Science:

• Computer Engineering
• Electrical Engineering
• Multimedia Systems

Background:

• Mobile devices dominate video streaming, driving significant internet data traffic.
• Memory power consumption is a critical bottleneck in mobile video processing.
• Current hardware-based power optimization methods incur substantial costs.

Purpose of the Study:

• To propose a novel video content-aware memory technique for power-quality trade-offs.
• To reduce power consumption in mobile video processing systems.
• To enhance energy efficiency without compromising the viewer's experience.

Main Methods:

• Developed viewer-centric models (decision tree, logistic regression) based on video macroblock characteristics.
• Implemented a viewer-aware bit-truncation technique for video storage.
• Enabled hardware adaptation for energy-quality trade-offs.

Main Results:

• The proposed technique effectively balances power consumption and video quality.
• Viewer-aware adaptation minimizes the impact on user experience.
• Achieved energy-quality adaptation in video storage.

Conclusions:

• Viewer-aware memory techniques offer a viable solution for power-quality trade-offs in mobile video.
• Content-adaptive strategies are crucial for efficient mobile video processing.
• This approach provides a cost-effective method for optimizing mobile device performance.