Research Article
Battery-Aware Wireless Video Delivery
@INPROCEEDINGS{10.1007/978-3-642-29222-4_36, author={Jianxin Sun and Dalei Wu and Song Ci}, title={Battery-Aware Wireless Video Delivery}, proceedings={Quality, Reliability, Security and Robustness in Heterogeneous Networks. 7th International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2010, and Dedicated Short Range Communications Workshop, DSRC 2010, Houston, TX, USA, November 17-19, 2010, Revised Selected Papers}, proceedings_a={QSHINE}, year={2012}, month={10}, keywords={multimedia video battery wireless communication system QoS distortion}, doi={10.1007/978-3-642-29222-4_36} }- Jianxin Sun
Dalei Wu
Song Ci
Year: 2012
Battery-Aware Wireless Video Delivery
QSHINE
Springer
DOI: 10.1007/978-3-642-29222-4_36
Abstract
Feasibility and popularity of mobile multimedia have made video communications between mobile devices a rising trend with a wide range of applications. However, two main problems have emerged. First: in most of mobile devices, the power-hungry multimedia processor relies on the battery as the only form of power resource and the characteristics of a battery keep changing as the discharging operation goes. How to wisely utilize the energy stored in batteries on mobile device becomes a critical issue in designing a wireless video communication system. Second: in order to achieve QoS on mobile devices, the requested video chip has to be displayed under a given standard of quality. Therefore, it is also necessary for the received video to satisfy the constraint of an acceptable level of distortion. To analyze and optimize the communication quality and energy consumption behavior of battery-driven wireless video communication systems, we propose an optimization framework which takes into account the characteristics of battery driven devices by considering the relation between energy consumption and capacity discharging behavior of battery. In our framework, the video coding and transmission parameters are jointly optimized to minimize the battery capacity consumption under a predefined level of expected received video distortion. Experimental results indicate the efficiency and effectiveness of the proposed optimization framework.