Energy-Efficient Layered Video Multicast over OFDM-Based Cognitive Radio Systems. (12th October 2015)

• Record Type: Journal Article
• Authors: Xu, Wenjun; Li, Shengyu; Xu, Yue; Feng, Zhiyong; Lin, Jiaru
• Other Names: Pan Miao Academic Editor.
• Is Part Of: International journal of distributed sensor networks. (2015)
• Publication Date: 2015-10-12
• Subjects: Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1155/2015/138328
• Languages: English
• ISSNs: 1550-1329

Abstract

Abstract : An energy-efficient layered video multicast (LVM) scheme for " bandwidth-hungry " video services is studied in OFDM-based cognitive radio (CR) systems, where the video data is encoded into a base layer and several enhancement layers with the former intended for all subscribers to guarantee the basic quality of reconstructed video and the latter aiming at the quality improvement for the promising users with good channel conditions. Moreover, in order to balance user experience maximization and power consumption minimization, a novel performance metric energy utility (EU) is proposed to measure the sum achieved quality of reconstructed video at all subscribers when unit transmit power is consumed. Our objective is to maximize the system EU by jointly optimizing the intersession/interlayer subcarrier assignment and subsequent power allocation. For this purpose, we first perform subcarrier assignment for base layer and enhancement layers using greedy algorithm and then present an optimal power allocation algorithm to maximize the achievable EU using fractional programming. Simulation results show that the proposed algorithms can adaptively capture the state variations of licensed spectrum and dynamically adjust the video transmission to exploit the scarce spectrum and energy resources adequately. Meanwhile, the system EU obtained in our algorithms is greatly improved over traditional spectrum efficiency (SE) and energy efficiency (EE) optimization models.