This document proposes a dynamic backlight scaling optimization technique for mobile streaming applications. It presents an optimal algorithm to dynamically adjust backlight levels for video frames based on video distortion, hardware limitations, and user perception. The algorithm is demonstrated on an HTC Desire mobile phone browsing YouTube videos. Experimental results show 18-31% backlight energy savings when the video distortion threshold is set to 0.9 and playback duration threshold is set to 10 seconds. The technique is presented as an example of a cloud-based mobile energy-saving service.

1. Dynamic Backlight Scaling Optimization
for Mobile Streaming Applications
Pi‐Cheng Hsui, Chun‐Han Lin, and Cheng‐Kang Hsieh
Research Center for Information Technology Innovation
Academia Sinica, Taiwan
International Symposium on Low Power Electronics and Design

2. Outline
• Introduction
• Dynamic Backlight Scaling Optimization
– An optimal algorithm
– Demonstration
• Performance Evaluation
– System deployment
– Experimental results
• Conclusion
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3. Outline
• Introduction
• Dynamic Backlight Scaling Optimization
– An optimal algorithm
– Demonstration
• Performance Evaluation
– System deployment
– Experimental results
• Conclusion
3

4. Research Motivation
• Mobile applications and services are having a
profound effect on people's lifestyles
• The energy consumption of mobile devices is a major
challenge in sustaining the applications and services.
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5. Possible Solutions
• Battery extenders • Cloud‐based Energy‐Saving
Services
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6. What is the use?
• With the service is applied, the service provider help reduce
the energy consumption of mobile devices when they access
Internet applications.
What application to start? What hardware to target?
CPU,
WIFI,
16.87%
LCD Screen, 24.62%
24.38%
Backlight,
34.12%
Cisco Forecast:
Video streams will account for 66% of Power distribution on HTC Desire
mobile data traffic by 2014 when browsing videos on YouTube
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7. Outline
• Introduction
• Dynamic Backlight Scaling Optimization
– An optimal algorithm
– Demonstration
• Performance Evaluation
– System deployment
– Experimental results
• Conclusion
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8. Dynamic Backlight Scaling Optimization for
Mobile Streaming Applications
• Dynamic backlight scaling
– Dynamically adjust backlight
levels for video frames
• A technical problem
– Determine appropriate levels
for a video subject to scaling
constraints
• Video distortion
• Hardware/software limitation
• User perception
• Etc.
• Contributions
– Problem formulation
– An optimal algorithm
– System implementation
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9. A Dynamic‐Programming Algorithm
Image frames in video
Video distortion
Hardware/software limitation
Backlight assignment
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10. Demonstration – HTC Desire
Approach validation
Performance evaluation
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11. Outline
• Introduction
• Dynamic Backlight Scaling Optimization
– An optimal algorithm
– Demonstration
• Performance Evaluation
– System deployment
– Experimental results
• Conclusion
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12. System Deployment
System architecture
Case studies
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13. Experimental Results
• Impact of video distortion on • Impact of duration on energy
energy savings savings
• 18‐31% when the distortion • 18‐31% when the duration
threshold is set at 0.9 threshold is set at 10
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14. Outline
• Introduction
• Dynamic Backlight Scaling Optimization
– An optimal algorithm
– Demonstration
• Performance Evaluation
– System deployment
– Experimental results
• Conclusion
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15. Conclusion
• We raise the concept of cloud‐based energy‐saving services
and have developed the dynamic backlight scaling service as
an example service.
– With the service is applied, an HTC Desire mobile phone can save 18‐
31% backlight energy when browsing videos on YouTube.
• We will release the mobile application program in the Android
Market and seek feedback to identify more issues in this
research direction.
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16. We are the Research Center for IT Innovation
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