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http://dx.doi.org/10.3837/tiis.2015.06.010

Energy Cognitive Dynamic Adaptive Streaming over HTTP  

Kim, Seohyang (Dept. Of Computer Science, Seoul National University)
Oh, Hayoung (School of Electronic and Engineering, Soongsil University)
Kim, Chongkwon (Dept. Of Computer Science, Seoul National University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.9, no.6, 2015 , pp. 2144-2159 More about this Journal
Abstract
CISCO VNI predicted an average annual growth rate of 66% for mobile video traffic between 2014 and 2019 and accordingly much academic research related to video streaming has been initiated. In video streaming, Adaptive Bitrate (ABR) is a streaming technique in which a source video is stored on a server at variable encoding rates and each streaming user requests the most appropriate video encoding rate considering their channel capacity. However, these days, ABR related studies are only focusing on real-time rate adaptation omitting energy efficiency though it is one of the most important requirement for mobile devices, which may cause dissatisfaction for streaming users. In this paper, we propose an energy efficient prefetching based dynamic adaptive streaming technique by considering the limited characteristics of the batteries used in mobile devices, in order to reduce the energy waste and provide a similar level of service in terms of the average video rate compared to the latest ABR streaming technique which does not consider the energy consumption. The simulation results is showing that our proposed scheme saves 65~68% of energy at the average global mobile download speed compared to the latest high performance ABR algorithm while providing similar rate adaptation performance.
Keywords
Mobile Network; Video Streaming; Energy Efficient Communication; ABR; MPEG-DASH;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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1 R. Trestian, A.-N. Moldovan, O. Ormond, G.-M. Muntean, “Energy Consumption Analysis of Video Streaming to Android Mobile Devices,” NOMS, 2012.
2 T. Stockhammer, “Dynamic Adaptive Streaming over HTTP - Standards and Design Principles,” ACM MMSys, 2011.
3 I. Sodagar, “The MPEG-DASH standard for multimedia streaming over the internet,” IEEE Multimedia, 2011.
4 J. Chen, R. Mahindra, M. A. Khojastepour, S. Rangarajan, M. Chiang, “A Scheduling Framework for Adaptive Video Delivery over Cellular Networks,” ACM MobiCom, 2013.
5 A. Chan, A. Pande, E. Baik, P. Mohapatra, “Temporal Quality Assessment for Mobile Videos,” ACM MobiCom, 2012.
6 J. Yoon, S. Banerjee, H. Zhang, S. Rangarajan, “MuVi: A Multicast Video Delivery Scheme for 4G Cellular Networks,” ACM MobiCom, 2012.
7 H. Cui, C. Luo, Chang W. C., F. Wu, “Robust Uncoded Video Transmission over Wireless Fast Fading Channel,” IEEE INFOCOM, 2014.
8 H. Cui, D. Qian, X. Zhang, I. You and X. Dong, “Optimizing the Joint Source/Network Coding for Video Streaming over Multi-hop Wireless Networks,” KSII Transactions on Internet and Information Systems, vol. 7, no. 4, 2013.
9 H. Shen and Q. Qiu, “User-Aware Energy Efficient Streaming Strategy for Smartphone Based Video Playback Applications,” IEEE DATE, 2013.
10 J. Lee, J. Hwang, N. Choi, and C. Yoo, “SVC-based Adaptive Video Streaming over Content-Centric Networking,” KSII Transactions on Internet and Information Systems, vol. 7, no. 10, pp. 2430-2447, 2013.   DOI
11 Y. Sanchez, T. Schierl, D. Hong, D. D. Vleeschauwer, Y. L. Louedec, “iDASH: Improved Dynaamic Adaptive Streaming over HTTP using Scalable Video Coding,” ACM MMSys, 2011.
12 R. Bhatia, T. V.Lakshman, A. Netravali, K. Sabnani, “Improving mobile video streaming with link aware scheduling and client caches,” IEEE INFOCOM, 2014.
13 http://www.benchbee.co.kr/
14 http://www.netindex.com/
15 L. Toni, R. Aparicio-Pardo, G. Simon, A. Blanc, and P. Frossard, “Optimal Set of Video Representations in Adaptive Streaming,” ACM MMSys, 2014.
16 G. Wang, K. Wu, Q. Zhang and L. M. Ni, “SimCast: Efficient Video Delivery in MU-MIMO WLANs,” IEEE INFOCOM, 2014.
17 L. Golubchik, S. Khuller, K.Mukherjee, Y. Yao, “To send or not to send: Reducing the cost of data transmission,” IEEE INFOCOM, 2013.
18 J. Huang, F. Qian, Al. Gerber, “A Close Examination of Performance and Power Characteristics of 4G LTE Networks,” MobiSys, 2012.
19 H. Oh, “A Robust Mobile Video Streaming in Heterogeneous Emerging Wireless Systems,” KSII Transactions on Internet and Information Systems, vol. 6, no. 9, 2012.
20 L. Zhou, R. Q. Hu, Y. Qian, and H.-H. Chen, “Energy-Spsectrum Efficiency Tradeoff for Video Streaming over Mobile Ad Hoc Networks,” IEEE Journal on Selected Areas in Communications, vol. 31, no. 5, pp. 981-991, 2013.   DOI
21 R. Li, B. Li, A. Eryilmaz, “Throughput-Optimal Wireless Scheduling with Regulated InterService Times,” IEEE INFOCOM, 2013.
22 R. Q. Hu and Y. Qian, “An Energy Efficient and Spectrum Efficient Wireless Heterogeneous Network Framework for 5G Systems,” vol. 52, no. 5, pp. 94-101, IEEE Communications Magazine, 2014.   DOI
23 http://www.cisco.com/c/en/us/solutions/service-provider/visual-networking-index-vni/index.html
24 M. A. Hoque, M. Siekkinen, J. K. Nurminen, “Using Crowd-Sourced Viewing Statistics to Save Energy in Wireless Video Streaming,” ACM MobiCom, 2013.
25 T.-Y. Huang, R. Johari, N.McKeown, M. Trunnel, M. Watson, “A Buffer-Based Approach to Rate Adaptation: Evidence from a Large Video Streaming Service,” ACM SIGCOMM, 2014.
26 X. Li, M. Dong, Z. Ma, F. Fernandes, “GreenTube: Power Optimization for Mobile Video Streaming via Dynamic Cache Management,” ACMMM, 2012.
27 F. Qian, Z.W, A. Gerber, Z. M.Mao, S. Sen, O. Spatscheck, “TOP: Tail Optimization Protocol for Cellular Radio Resource Allocation,” ICNP, 2010.
28 F. Qian, Z. Wang, A. Gerber, Z. M. Mao, S. Sen, O. Spatscheck, “Characterizing Radio Resource Allocation for 3G Networks,” IMC, 2010.
29 https://www.youtube.com/yt/playbook/yt-analytics.htm