Wireless Personal Communications

, Volume 87, Issue 2, pp 565–592 | Cite as

Evaluation of Video Transmission Energy Consumption and Quality

  • Vitor Bernardo
  • Vitor Fonseca
  • Marilia Curado
  • Torsten Braun
Article

Abstract

The widespread use of wireless enabled devices and the increasing capabilities of wireless technologies has promoted multimedia content access and sharing among users. However, the quality perceived by the users still depends on multiple factors such as video characteristics, device capabilities, and link quality. While video characteristics include the video time and spatial complexity as well as the coding complexity, one of the most important device characteristics is the battery lifetime. There is the need to assess how these aspects interact and how they impact the overall user satisfaction. This paper advances previous works by proposing and validating a flexible framework, named EViTEQ, to be applied in real testbeds to satisfy the requirements of performance assessment. EViTEQ is able to measure network interface energy consumption with high precision, while being completely technology independent and assessing the application level quality of experience. The results obtained in the testbed show the relevance of combined multi-criteria measurement approaches, leading to superior end-user satisfaction perception evaluation .

Keywords

Energy efficiency Video IEEE 802.11 Wireless Quality of experience Testbed 

Notes

Acknowledgments

This work was partially funded by the projects iCIS (CENTRO-07-ST24-FEDER-002003) and SusCity: Urban data driven models for creative and resourceful urban transitions (MITP-TB/CS/0026/2013), and by COST Actions IC0906 (WiNeMO) and IC1303 (AAPELE). The Portuguese National Foundation for Science and Technology (FCT) supported the first author through a Doctoral Grant (SFRH/BD/66181/2009).

References

  1. 1.
    Cisco, Inc. (2015). Cisco visual networking index: Global mobile data traffic forecast update 2014–2019 white paper, 2015.Google Scholar
  2. 2.
    Ivanov, S., Botvich, D., & Balasubramaniam, S. (2013). Joint delay and energy model for IEEE 802.11 networks. Wireless Networks. doi: 10.1007/s11276-013-0669-7.
  3. 3.
    Elmachkour, M., Kobbane, A., Sabir, E., Ben-othman, J., & El koutbi, M. (2014). Data traffic-based analysis of delay and energy consumption in cognitive radio networks with and without resource reservation. International Journal of Communication Systems. doi: 10.1002/dac.2764.
  4. 4.
    Bernardo, V., & Curado, M. (2012). A methodology for assessing video transmission energy consumption and quality. In 2012 IEEE international conference on communications (ICC) (pp. 6308–6313). doi: 10.1109/ICC.2012.6364945.
  5. 5.
    Balasubramanian, N., Balasubramanian, A., & Venkataramani, A. (2009). Energy consumption in mobile phones: A measurement study and implications for network applications. In Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference (IMC ’09) (pp. 280–293). New York, NY, USA: ACM. doi: 10.1145/1644893.1644927.
  6. 6.
    Wang, L., & Manner, J. (2010). Energy consumption analysis of wlan, 2G and 3G interfaces. In Green computing and communications (GreenCom), 2010 IEEE/ACM international conference on international conference on cyber, physical and social computing (CPSCom) (pp. 300–307). doi: 10.1109/GreenCom-CPSCom.2010.81.
  7. 7.
    Rice, A., & Hay, S. (2010). Measuring mobile phone energy consumption for 802.11 wireless networking. Pervasive and Mobile Computing, 6, 593–606. doi: 10.1016/j.pmcj.2010.07.005.CrossRefGoogle Scholar
  8. 8.
    Trestian, R., Moldovan, A. N., Ormond, O., & Muntean, G. (2012). Energy consumption analysis of video streaming to android mobile devices. In 2012 IEEE network operations and management symposium (NOMS) (pp. 444–452). doi: 10.1109/NOMS.2012.6211929.
  9. 9.
    Shih, E., Bahl, P., & Sinclair, M. J. (2002). Wake on wireless: An event driven energy saving strategy for battery operated devices. In Proceedings of the 8th annual international conference on Mobile computing and networking (MobiCom ’02) (pp. 160–171). New York, NY, USA: ACM. doi: 10.1145/570645.570666.
  10. 10.
    Vergara, E., & Nadjm-Tehrani, S. (2013). Energybox: A trace-driven tool for data transmission energy consumption studies. In J. M. Pierson, G. Da Costa, & L. Dittmann (Eds.), Energy efficiency in large scale distributed systems, lecture notes in computer science (pp. 19–34). Berlin: Springer. doi: 10.1007/978-3-642-40517-4_2.
  11. 11.
    Li, Y., Reisslein, M., & Chakrabarti, C. (2009). Energy-efficient video transmission over a wireless link. IEEE Transactions on Vehicular Technology, 58(3), 1229–1244. doi: 10.1109/TVT.2008.927720.CrossRefGoogle Scholar
  12. 12.
    Lee, S., Koo, J., & Chung, K. (2010). Content-aware rate control scheme to improve the energy efficiency for mobile IPTV. In 2010 Digest of technical papers international conference on consumer electronics (ICCE) (pp. 445 –446). doi: 10.1109/ICCE.2010.5418958.
  13. 13.
    Abdel Khalek, A., & Dawy, Z. (2011). Energy-efficient cooperative video distribution with statistical qos provisions over wireless networks. IEEE Transactions on Mobile Computing. doi: 10.1109/TMC.2011.127.
  14. 14.
    Hoque, M., Siekkinen, M., Nurminen, J., & Aalto, M. (2013). Dissecting mobile video services: An energy consumption perspective. In 2013 IEEE 14th international symposium and workshops on a world of wireless, mobile and multimedia networks (WoWMoM) (pp. 1–11). doi: 10.1109/WoWMoM.2013.6583384.
  15. 15.
    Yuan, W., Nahrstedt, K., Adve, S., Jones, D., & Kravets, R. (2006). Grace-1: Cross-layer adaptation for multimedia quality and battery energy. IEEE Transactions on Mobile Computing, 5(7), 799–815. doi: 10.1109/TMC.2006.98.CrossRefGoogle Scholar
  16. 16.
    Winkler, S., & Mohandas, P. (2008). The evolution of video quality measurement: From psnr to hybrid metrics. IEEE Transactions on Broadcasting, 54(3), 660–668. doi: 10.1109/TBC.2008.2000733.CrossRefGoogle Scholar
  17. 17.
    De Moor, K., Ketyko, I., Joseph, W., Deryckere, T., De Marez, L., Martens, L., et al. (2010). Proposed framework for evaluating quality of experience in a mobile, testbed-oriented living lab setting. Mobile Networks and Applications, 15, 378–391. doi: 10.1007/s11036-010-0223-0.CrossRefGoogle Scholar
  18. 18.
    Bernardo, V., Pentikousis, K., Pinola, J., Piri, E., & Curado, M. (2009). Multi-client video streaming over wirelessman-ofdma. In Proceedings of the 4th ACM workshop on performance monitoring and measurement of heterogeneous wireless and wired networks (PM2HW2N ’09) (pp. 46–53). New York, NY, USA: ACM. doi: 10.1145/1641913.1641920.
  19. 19.
    ITU-T. (2014). ITU-T recommendation H.264: Advanced video coding for generic audiovisual services (v9).Google Scholar
  20. 20.
    Apostolopoulos, J., Tan, W., & Wee, S. (2002). Video streaming: Concepts, algorithms, and systems. Tech. Rep. 2002-260, HP Laboratories.Google Scholar
  21. 21.
    Rao, K., Kim, D., & Hwang, J. (2014). H.264/mpeg-4 advanced video coding. In Video coding standards, signals and communication technology (pp. 99–124). Netherlands: Springer. doi: 10.1007/978-94-007-6742-3_4.
  22. 22.
    Wiegand, T., Sullivan, G., Bjontegaard, G., & Luthra, A. (2003). Overview of the h.264/avc video coding standard. IEEE Transactions on Circuits and Systems for Video Technology, 13(7), 560–576. doi: 10.1109/TCSVT.2003.815165.CrossRefGoogle Scholar
  23. 23.
    Wang, Z., Bovik, A., Sheikh, H., & Simoncelli, E. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600–612. doi: 10.1109/TIP.2003.819861.CrossRefGoogle Scholar
  24. 24.
    Wang, Z., Bovik, A., Sheikh, H., & Simoncelli, E. (2015). The SSIM index for image quality assessment. http://www.ece.uwaterloo.ca/~z70wang/research/ssim. Accessed November 01 2015.
  25. 25.
    Singh, R., & Aggarwal, N. (2014). State of the art and research issues in video quality assessment. In 2014 Recent advances in engineering and computational sciences (RAECS) (pp. 1–6). doi: 10.1109/RAECS.2014.6799583.
  26. 26.
    Pinson, M., & Wolf, S. (2004). A new standardized method for objectively measuring video quality. IEEE Transactions on Broadcasting, 50(3), 312–322. doi: 10.1109/TBC.2004.834028.CrossRefGoogle Scholar
  27. 27.
    ITU-T. (2000). ITU-T recommendation J.143—User requirements for objective perceptual video quality measurements in digital cable television.Google Scholar
  28. 28.
    VQEG. (2015). Video Quality Experts Group—Final report from the Video Quality Experts Group on the validation of objective models of video quality assessment, phase II. http://www.vqeg.org. Accessed 31 Oct 2015.
  29. 29.
    tcpdump. (2015). tcpdump, a powerful command-line packet analyzer; and libpcap, a portable C/C++ library for network traffic capture. http://www.tcpdump.org. Accessed Oct ober 30 2015.
  30. 30.
    Klaue, J., Rathke, B., & Wolisz, A. (2003). EvalVid—A framework for video transmission and quality evaluation. In Proceedings of the 13th conference on modelling techniques and tools for computer performance evaluation, lecture notes in computer science (pp. 255–272). Berlin: Springer. doi: 10.1007/b12028.
  31. 31.
    Ucar, I., Navarro-Ortiz, J., Ameigeiras, P., & Lopez-Soler, J. (2012). Video tester—A multiple-metric framework for video quality assessment over ip networks. In 2012 IEEE international symposium on broadband multimedia systems and broadcasting (BMSB) (pp. 1–5). doi: 10.1109/BMSB.2012.6264243.
  32. 32.
    IEEE. (2004). Standard digital interface for programmable instrumentation—Part 2: Codes, formats, protocols and common commands (adoption of ieee std 488.2-1992). IEC 60488-2 First edition 2004-05; IEEE 4882 (pp. 1–261). doi: 10.1109/IEEESTD.2004.95390.
  33. 33.
    Carbone, M., & Rizzo, L. (2010). Dummynet revisited. SIGCOMM Computer Communication Review, 40, 12–20. doi: 10.1145/1764873.1764876.CrossRefGoogle Scholar
  34. 34.
    Hall, T., Hurtig, P., & Anna Brunstrom, J. G. (2012). Performance evaluation of kaunet in physical and virtual emulation environments. Tech. Rep. No 2012:32, Karlstad University.Google Scholar
  35. 35.
    MSU Graphics & Media Lab (Video Group). (2015). MSU video quality measurement tool. http://compression.ru/video. Accessed November 01 2015.
  36. 36.
    Keimel, C., Redl, A., & Diepold, K. (2012). The tum high definitionvideo datasets. In Forth international workshop on quality of multimedia experience (QoMEX 2012).Google Scholar
  37. 37.
    Deng, C., Lin, W., Sung Lee, B., Lau, C. T., & Sun, M. T. (2011). Performance analysis, parameter selection and extensions to h.264/avc for high resolution video coding. Journal of Visual Communication and Image Representation, 22(8), 749–759. doi: 10.1016/j.jvcir.2011.01.004. emerging Techniques for High Performance Video Coding.CrossRefGoogle Scholar
  38. 38.
    FFmpeg. (2015). FFmpeg: A complete, cross-platform solution to record, convert and stream audio and video. https://www.ffmpeg.org. Accessed November 03 2015.
  39. 39.
    Correl, K. (2015). PTP daemon (PTPd). http://ptpd.sourceforge.net. Accessed November 01 2015.
  40. 40.
    Correll, K., & Barendt, N. (2006). Design considerations for software only implementations of the IEEE 1588 precision time protocol. In Conference on IEEE 1588 standard for a precision clock synchronization protocol for networked measurement and control systems.Google Scholar
  41. 41.
    Wang, X., Han, Q., Guan, X., & Ma, K. (2013). Price-based interference management in dense femtocell systems. International Journal of Communication Systems. doi: 10.1002/dac.2636.

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Vitor Bernardo
    • 1
  • Vitor Fonseca
    • 1
  • Marilia Curado
    • 1
  • Torsten Braun
    • 2
  1. 1.Center for Informatics and SystemsUniversity of CoimbraCoimbraPortugal
  2. 2.Institute of Computer ScienceUniversity of BernBernSwitzerland

Personalised recommendations