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DualEMC: energy efficient mobile multimedia communication with cloud

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Abstract

Video streaming has become one of the most popular networked applications and, with the increased bandwidth and computation power of mobile devices, anywhere and anytime streaming has become a reality. Unfortunately, it remains a challenging task to compress high-quality video in real-time in such devices given the excessive computation and energy demands of compression. On the other hand, transmitting the raw video is simply unaffordable from both energy and bandwidth perspective. In this paper, we propose DualEMC, a novel cloud-assisted video compression mechanism for mobile devices. DualEMC leverages the abundant cloud server resources for motion estimation (ME), which is known to be the most computation-intensive step in video compression, accounting for over 90 % of the computation time. With DualEMC, a mobile device selects and uploads only the key information of each picture frame to cloud servers for mesh-based ME, eliminating most of the local computation operations. We develop smart algorithms to identify the key mesh nodes, resulting in minimum distortion and data volume for uploading. Our simulation results demonstrate that DualEMC saves almost 30 % energy for video compression and transmission.

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References

  1. Altunbasak, Y., & Tekalp, A. M. (1997). Occlusion-adaptive, content-based mesh design and forward tracking. IEEE Transactions on Image Processing, 6(9), 1270–1280.

    Article  Google Scholar 

  2. Amazon. Amazon High Performance Computing (HPC). http://aws.amazon.com/hpc-applications/.

  3. Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R. H., Konwinski, A., Lee, G., Patterson, D. A., Rabkin, A., Stoica, I., et al. (2009). Above the clouds: A berkeley view of cloud computing. Technical Report UCB/EECS-2009-28, EECS Department, University of California, Berkeley.

  4. Badawy, W., & Bayoumi, M. (2002). A multiplication-free algorithm and a parallel architecture for affine transformation. The Journal of VLSI Signal Processing, 31(2), 173–184.

    Article  Google Scholar 

  5. Badawy, W., & Bayoumi, M. A. (2002). A low power VLSI architecture for mesh-based video motion tracking. IEEE Transactions on Circuits and Systems II Analog and Digital Signal Processing, 49(7), 488–504.

    Article  Google Scholar 

  6. Bahari, A., Arslan, T., & Erdogan, A. T. (2009). Low-power H. 264 video compression architectures for mobile communication. IEEE Transactions on Circuits and Systems for Video Technology, 19(9), 1251–1261.

    Article  Google Scholar 

  7. 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, (pp. 280–293).

  8. Cheng, H. P., Shen, Y. C., Wu, J. L., & Aizawa, K. (2011). High efficient distributed video coding with parallelized design for cloud computing. In Proceedings of the 19th ACM international conference on Multimedia, (pp. 1257–1260).

  9. Dudon, M., Avaro, O., & Roux, C. (1997). Triangular active mesh for motion estimation. Signal Processing Image Communication, 10, 21–41.

    Article  Google Scholar 

  10. Huang, Z., Mei, C., Li, L. E., & Woo, T. (2011). CloudStream: Delivering high-quality streaming videos through a cloud-based SVC proxy. In INFOCOM, 2011 Proceedings IEEE, (pp. 201–205).

  11. Intel. Intel Performance Counter Monitor. http://software.intel.com/en-us/articles/intel-performance-counter-monitor/.

  12. Jackson, E. S., & Peplow, R. (2003). Video Compression System for Mobile Devices. RN, 2(2).

  13. Kubasov, D., & Guillemot, C. (2006). Mesh-based motion-compensated interpolation for side information extraction in distributed video coding. IEEE International Conference on Image Processing, 2006, 261–264.

    Google Scholar 

  14. Lai, Y. X., Lai, C. F., Hu, C. C., Chao, H. C., & Huang, Y. M. (2011). A personalized mobile IPTV system with seamless video reconstruction algorithm in cloud networks. International Journal of Communication Systems, 24(10), 1375–1387.

    Article  Google Scholar 

  15. Lin, C. H., Shieh, C. K., Ke, C. H., Chilamkurti, N. K., & Zeadally, S. (2009). An adaptive cross-layer mapping algorithm for MPEG-4 video transmission over IEEE 802.11e WLAN. Telecommunication Systems, 42(3—-4), 223–234.

    Article  Google Scholar 

  16. Liu, F., Shen, S., Li, B., Li, B., Yin, H., & Li, S. (2011). Novasky: Cinematic-quality VoD in a P2P storage cloud. INFOCOM, 2011 Proceedings IEEE, (pp. 936–944).

  17. Miao, D., Zhu, W., Luo, C., & Chen, C.W. (2011). Resource allocation for cloud-based free viewpoint video rendering for mobile phones. Proceedings of the 19th ACM international conference on Multimedia, (pp. 1237–1240).

  18. Nakaya, Y., & Harashima, H. (1994). Motion compensation based on spatial transformations. IEEE Transactions on Circuits and Systems for Video Technology, 4(3), 339–356.

    Article  Google Scholar 

  19. Peixoto, E., de Queiroz, R. L., & Mukherjee, D. (2008). Mobile video communications using a Wyner-Ziv transcoder. Symposium on Electronic Imaging, Visual Communications and Image Processing (SPIE), San Jose, CA, USA.

  20. Sayed, M., & Badawy, W. (2004). A novel motion estimation method for mesh-based video motion tracking. In IEEE International Conference on Acoustics, Speech, and Signal Processing, 2004. Proceedings (ICASSP’04), volume 3, (pp. iii-337).

  21. Shamir, A. (2008). A survey on mesh segmentation techniques. Computer graphics forum, 27(6), 1539–1556.

    Article  Google Scholar 

  22. Singh, K., & Davids, C. (2011). Flash-based audio and video communication in the cloud. Arxiv preprint arXiv:1107.0011.

  23. SourceForge. Java H.264 Encoder. http://sourceforge.net/projects/h264avcjavaenco/.

  24. Sullivan, G. J., & Wiegand, T. (2005). Video compression-from concepts to the H. 264/AVC standard. Proceedings of the IEEE, 93(1), 18–31.

    Article  Google Scholar 

  25. Valette, S., Magnin, I., & Prost, R. R. (2004). Mesh-based video objects tracking combining motion and luminance discontinuities criteria. Signal processing, 84(7), 1213–1224.

    Article  Google Scholar 

  26. Wang, T., & Ostermann, J. (1988). Evaluation of mesh-based motion estimation in H. 263-like coders. IEEE Transactions on Circuits and Systems for Video Technology, 8(3), 243–252.

    Article  Google Scholar 

  27. Wang, Y., & Lee, O. (1994). Active mesh-a feature seeking and tracking image sequence representation scheme. IEEE Transactions on Image Processing, 3(5), 610–624.

    Article  Google Scholar 

  28. Wang, Y., Lee, O., & Vetro, A. (1996). Use of two-dimensional deformable mesh structures for video coding, part II: The analysis problem and a region-based coder employing an active mesh representation. IEEE Transactions on circuits and systems for video technology, 6(6), 647–659.

    Article  Google Scholar 

  29. Wang, Y., Ostermann, J., & Zhang, Y. Q. (2002). Video processing and communications (Vol. 1). Upper Saddle River: Prentice Hall.

    Google Scholar 

  30. Wu, Y., Wu, C., Li, B., Qiu, X., & Lau, F. (2011). CloudMedia: When cloud on demand meets video on demand. 2011 31st International Conference on Distributed Computing Systems (ICDCS), (pp. 268–277).

  31. YouTube. YouTube Statistics. http://www.youtube.com/t/press_statistics.

  32. Yuan, W., & Nahrstedt, K. (2006). Energy-efficient CPU scheduling for multimedia applications. ACM Transactions on Computer Systems (TOCS), 24(3), 292–331.

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Computing Science, Simon Fraser University, 8888 University Dr, Burnaby, BC, V5A 1S6, Canada

    Yuan Zhao, Lei Zhang, Xiaoqiang Ma & Jiangchuan Liu

  2. School of Computer Science and Technology, Huazhong University of Science and Technology, 1037 Luoyu Rd, Hongshan, Wuhan, Hubei, China

    Hongbo Jiang

Authors
  1. Yuan Zhao
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  2. Lei Zhang
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  3. Xiaoqiang Ma
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  4. Jiangchuan Liu
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Correspondence to Yuan Zhao.

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Zhao, Y., Zhang, L., Ma, X. et al. DualEMC: energy efficient mobile multimedia communication with cloud. Telecommun Syst 60, 85–94 (2015). https://doi.org/10.1007/s11235-014-9923-2

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  • DOI: https://doi.org/10.1007/s11235-014-9923-2

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