This Article 
 Bibliographic References 
 Add to: 
Compressed-Sensing-Enabled Video Streaming for Wireless Multimedia Sensor Networks
June 2012 (vol. 11 no. 6)
pp. 1060-1072
Scott Pudlewski, State University of New York (SUNY), Buffalo
Tommaso Melodia, State University of New York (SUNY), Buffalo
Arvind Prasanna, State University of New York (SUNY), Buffalo
This paper presents the design of a networked system for joint compression, rate control and error correction of video over resource-constrained embedded devices based on the theory of Compressed Sensing (CS). The objective of this work is to design a cross-layer system that jointly controls the video encoding rate, the transmission rate, and the channel coding rate to maximize the received video quality. First, compressed sensing-based video encoding for transmission over Wireless Multimedia Sensor Networks (WMSNs) is studied. It is shown that compressed sensing can overcome many of the current problems of video over WMSNs, primarily encoder complexity and low resiliency to channel errors. A rate controller is then developed with the objective of maintaining fairness among different videos while maximizing the received video quality. It is shown that the rate of Compressed Sensed Video (CSV) can be predictably controlled by varying only the compressed sensing sampling rate. It is then shown that the developed rate controller can be interpreted as the iterative solution to a convex optimization problem representing the optimization of the rate allocation across the network. The error resiliency properties of compressed sensed images and videos are then studied, and an optimal error detection and correction scheme is presented for video transmission over lossy channels. Finally, the entire system is evaluated through simulation and test bed evaluation. The rate controller is shown to outperform existing TCP-friendly rate control schemes in terms of both fairness and received video quality. The test bed results show that the rates converge to stable values in real channels.

[1] S. Pudlewski, T. Melodia, and A. Prasanna, “C-DMRC: Compressive Distortion-Minimizing Rate Control for Wireless Multimedia Sensor Networks,” Proc. IEEE Seventh Ann. Comm. Soc. Conf. Sensor, Mesh and Ad Hoc Comm. and Networks (SECON), June 2010.
[2] I.F. Akyildiz, T. Melodia, and K.R. Chowdhury, “A Survey on Wireless Multimedia Sensor Networks,” Computer Networks, vol. 51, no. 4, pp. 921-960, Mar. 2007.
[3] S. Soro and W. Heinzelman, “A Survey of Visual Sensor Networks,” Advances in Multimedia, vol. 2009, article number 640386, 2009.
[4] Y. Gu, Y. Tian, and E. Ekici, “Real-Time Multimedia Processing in Video Sensor Networks,” Signal Processing: Image Comm. J., vol. 22, no. 3, pp. 237-251, Mar. 2007.
[5] “Advanced Video Coding for Generic Audiovisual Services,” ITU-T Recommendation H.264, 2010.
[6] T. Wiegand, G.J. Sullivan, G. Bjntegaard, and A. Luthra, “Overview of the H.264/AVC Video Coding Standard,” IEEE Trans. Circuits and Systems for Video Technology, vol. 13, no. 7, pp. 560-576, July 2003.
[7] J. Ostermann, J. Bormans, P. List, D. Marpe, M. Narroschke, F. Pereira, T. Stockhammar, and T. Wedi, “Video Coding with H.264/AVC: Tools, Performance, and Complexity,” IEEE Circuits and System Magazine, vol. 4, no. 1, pp. 7-28, Jan.-Mar. 2004.
[8] T. Wiegand, G.J. Sullivan, J. Reichel, H. Schwarz, and M. Wien, “Joint Draft 11 of SVC Amendment,” Doc. JVT-X201, July 2007.
[9] Y. Wang, S. Wenger, J. Wen, and A. Katsaggelos, “Error Resilient Video Coding Techniques,” IEEE Signal Processing Magazine, vol. 17, no. 4, pp. 61-82, July 2000.
[10] B. Girod, A. Aaron, S. Rane, and D. Rebollo-Monedero, “Distributed Video Coding,” Proc. IEEE, vol. 93, no. 1, pp. 71-83, 2005.
[11] A. Wyner and J. Ziv, “The Rate-Distortion Function for Source Coding with Side Information at the Decoder,” IEEE Trans. Information Theory, vol. 22, no. 1, pp. 1-10, Jan. 1976.
[12] A. Aaron, E. Setton, and B. Girod, “Towards Practical Wyner-Ziv Coding of Video,” Proc. IEEE Int'l Conf. Image Processing, Sept. 2003.
[13] A. Aaron, S. Rane, R. Zhang, and B. Girod, “Wyner-Ziv Coding for Video: Applications to Compression and Error Resilience,” Proc. IEEE Data Compression Conf. (DCC), pp. 93-102, Mar. 2003.
[14] T. Sheng, G. Hua, H. Guo, J. Zhou, and C.W. Chen, “Rate Allocation for Transform Domain Wyner-Ziv Video Coding without Feedback,” Proc. ACM Int'l Conf. Multimedia, Oct. 2008.
[15] D. Donoho, “Compressed Sensing,” IEEE Trans. Information Theory, vol. 52, no. 4, pp. 1289-1306, Apr. 2006.
[16] E. Candes, J. Romberg, and T. Tao, “Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Frequency Information,” IEEE Trans. Information Theory, vol. 52, no. 2, pp. 489-509, Feb. 2006.
[17] E.J. Candes, J. Romberg, and T. Tao, “Stable Signal Recovery from Incomplete and Inaccurate Measurements,” Comm. Pure and Applied Math., vol. 59, no. 8, pp. 1207-1223, Aug. 2006.
[18] E. Candes and T. Tao, “Near-Optimal Signal Recovery from Random Projections and Universal Encoding Strategies?” IEEE Trans. Information Theory, vol. 52, no. 12, pp. 5406-5425, Dec. 2006.
[19] M. Wakin, J. Laska, M. Duarte, D. Baron, S. Sarvotham, D. Takhar, K. Kelly, and R. Baraniuk, “Compressive Imaging for Video Representation and Coding,” Proc. Picture Coding Symp., Apr. 2006.
[20] J. Romberg, “Imaging via Compressive Sampling,” IEEE Signal Processing Magazine, vol. 25, no. 2, pp. 14-20, Mar. 2008.
[21] M. Duarte, M. Davenport, D. Takhar, J. Laska, T. Sun, K. Kelly, and R. Baraniuk, “Single-Pixel Imaging via Compressive Sampling,” IEEE Signal Processing Magazine, vol. 25, no. 2, pp. 83-91, Mar. 2008.
[22] Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, “Image Quality Assessment: From Error Visibility to Structural Similarity,” IEEE Trans. Image Processing, vol. 13, no. 4, pp. 600-612, Apr. 2004.
[23] S.S. Hemami and A.R. Reibman, “No-Reference Image and Video Quality Estimation: Applications and Human-motivated Design,” Signal Processing: Image Comm., vol. 25, no. 7, pp. 469-481, 2010.
[24] S. Chikkerur, V. Sundaram, M. Reisslein, and L.J. Karam, “Objective Video Quality Assessment Methods: A Classification, Review, and Performance Comparison,” IEEE Trans. Broadcasting, vol. 57, no. 2, pp. 165-182, June 2011.
[25] M. Allman, V. Paxson, and W. Stevens, “TCP Congestion Control,” IETF RFC 2581, 2010.
[26] K. Tan, J. Song, Q. Zhang, and M. Sridharan, “A Compound TCP Approach for High-Speed and Long Distance Networks,” Proc. IEEE INFOCOM, pp. 1-12, Apr. 2006.
[27] W.T. Tan and A. Zakhor, “Real-Time Internet Video Using Error Resilient Scalable Compression and TCP-Friendly Transport Protocol,” IEEE Trans. Multimedia, vol. 1, no. 2, pp. 172-186, June 1999.
[28] M. Handley, S. Floyd, J. Padhye, and J. Widmer, “TCP Friendly Rate Control (TFRC): Protocol Specification,” IETF RFC 3448, 2010.
[29] S. Floyd, M. Handley, J. Padhye, and J. Widmer, “Equation-Based Congestion Control for Unicast Applications,” SIGCOMM Computer Comm. Rev., vol. 30, pp. 43-56, Aug. 2000.
[30] O.B. Akan and I.F. Akyildiz, “ARC: The Analytical Rate Control Scheme for Real-Time Traffic in Wireless Networks,” IEEE/ACM Trans. Networking, vol. 12, no. 4, pp. 634-644, Aug. 2004.
[31] O. Akan, “Performance of Transport Protocols for Multimedia Communications in Wireless Sensor Networks,” IEEE Comm. Letters, vol. 11, no. 10, pp. 826-828, Oct. 2007.
[32] K. Stuhlmuller, N. Farber, M. Link, and B. Girod, “Analysis of Video Transmission over Lossy Channels,” IEEE J. Selected Areas in Comm., vol. 18, no. 6, pp. 1012-1032, June 2000.
[33] Q. Zhang, W. Zhu, and Y. Zhang, “End-to-End QoS for Video Delivery over Wireless Internet,” Proc. IEEE, vol. 93, no. 1, pp. 123-134, Jan. 2005.
[34] D. Wu, Y.T. Hou, and Y.Q. Zhang, “Transporting Real-Time Video over the Internet: Challenges and Approaches,” Proc. IEEE, vol. 88, no. 12, pp. 1855-1877, Dec. 2000.
[35] V. Stankovic, L. Stankovic, and S. Cheng, “Compressive Video Sampling,” Proc. European Signal Processing Conf., Aug. 2008.
[36] J. Park and M. Wakin, “A Multiscale Framework for Compressive Sensing of Video,” Proc. Picture Coding Symp., May 2009.
[37] R. Marcia and R. Willett, “Compressive Coded Aperture Video Reconstruction,” Proc. European Signal Processing Conf., Aug. 2008.
[38] L.W. Kang and C.S. Lu, “Distributed Compressive Video Sensing,” Proc. IEEE Int'l Conf. Acoustics, Speech and Signal Processing (ICASSP), pp. 1169-1172, 2009.
[39] H.W. Chen, L.W. Kang, and C.S. Lu, “Dynamic Measurement Rate Allocation for Distributed Compressive Video Sensing,” Proc. SPIE Visual Comm. and Image Processing, Special Session on Compressed Sensing and Sparse Representation, 2010.
[40] H.W. Chen, L.W. Kang, and C.S. Lu, “Dictionary Learning-Based Distributed Compressive Video Sensing,” Proc. Picture Coding Symp., pp. 210-213, 2010.
[41] T. Do, Y. Chen, D. Nguyen, N. Nguyen, L. Gan, and T. Tran, “Distributed Compressed Video Sensing,” Proc. IEEE 16th Int'l Conf. Image Processing (ICIP), pp. 1393-1396, 2009.
[42] A. Bruckstein, D. Donoho, and M. Elad, “From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images,” SIAM Rev., vol. 51, pp. 34-81, rapid post, 2007.
[43] S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge Univ., Mar. 2004.
[44] I.E. Nesterov and A. Nemirovskii, Interior-Point Polynomial Algorithms in Convex Programming. SIAM, 1994.
[45] M. Zhu and T. Chan, “An Efficient Primal-Dual Hybrid Gradient Algorithm for Total Variation Image Restoration,” Technical Report UCLA CAM 08-34, 2008.
[46] A. Graps, “An Introduction to Wavelets,” IEEE Computational Science and Eng., vol. 2, no. 2, pp. 50-61, 1995.
[47] S. Rein and M. Reisslein, “Performance Evaluation of the Fractional Wavelet Filter: A Low-Memory Image Wavelet Transform for Multimedia Sensor Networks,” Ad Hoc Networks, vol. 9, pp. 482-496, B7576-50S8PP0-1/2fb2518d9e84aa1d10f8f88b15a857fb5 , 2010.
[48] L. Gan, T. Do, and T.D. Tran, “Fast Compressive Imaging Using Scrambled Block Hadamard Ensemble,” Proc. European Signal Processing Conf., rapid post, 2008.
[49] M.A.T. Figueiredo, R.D. Nowak, and S.J. Wright, “Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems,” IEEE J. Selected Topics in Signal Processing, Special Issue on Convex Optimization Methods for Signal Processing, vol. 1, no. 4, pp. 586-598, Dec. 2007.
[50] D.L. Donoho, Y. Tsaig, I. Drori, and J.L. Starck, “Sparse Solution of Underdetermined Linear Equations by Stagewise Orthogonal Matching Pursuit,” Discovery, vol. 114, pp. 1-39, Mar. 2006.
[51] USC Signal and Image Processing Inst.,, 2012.
[52] T. Melodia and S. Pudlewski, “A Case for Compressive Video Streaming in Wireless Multimedia Sensor Networks,” IEEE COMSOC MMTC E-Letter, vol. 4, no. 9, Oct. 2009.
[53] J. Hagenauer, “Rate-Compatible Punctured Convolutional Codes (RCPC Codes) and their Applications,” IEEE Trans. Comm., vol. 36, no. 4, pp. 389-400, Apr. 1988.
[54] C.E. Perkins, E.M. Belding-Royer, and S. Das, “Ad Hoc on Demand Distance Vector (AODV) Routing,” IETF RFC 3561, 2010.
[55] R. Jain, D.M. Chiu, and W. Hawe, “A Quantitative Measure of Fairness and Discrimination for Resource Allocation in Shared Computer Systems,” DEC Research Report TR-301, Sept. 1984.
[56] Arizona State Univ. Video Traces Research Group,, 2012.
[57] M. Ettus, “Building Software Defined Radios: The USRP Product Family,” Product Brochure, July 2009.
[58] The GNU Radio Project, http://gnuradio.orgtrac, 2012.
[59] F. Bellard, http:/, 2012.
[60] M. Grant and S. Boyd “CVX: Matlab Software for Disciplined Convex Programming, Version 1.21,” http://cvxr.comcvx, 2010.
[61] J. Sturm, “Using SeDuMi 1.02, a MATLAB Toolbox for Optimization over Symmetric Cones,” , Optimization Methods and Software, vol. 1, pp. 625-653, 1999.
[62] S.H. Low, L.L. Peterson, and L. Wang, “Understanding TCP Vegas: a Duality Model,” J. ACM, vol. 49, pp. 207-235, Mar. 2002.
[63] S. Low and D. Lapsley, “Optimization Flow Control, I: Basic Algorithm and Convergence,” IEEE/ACM Trans. Networking, vol. 7, no. 6, pp. 861-874, Dec. 1999.

Index Terms:
Compressed sensing, optimization, multimedia content, congestion control, sensor networks.
Scott Pudlewski, Tommaso Melodia, Arvind Prasanna, "Compressed-Sensing-Enabled Video Streaming for Wireless Multimedia Sensor Networks," IEEE Transactions on Mobile Computing, vol. 11, no. 6, pp. 1060-1072, June 2012, doi:10.1109/TMC.2011.175
Usage of this product signifies your acceptance of the Terms of Use.