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Mobile Networks and Applications

, Volume 21, Issue 6, pp 1002–1012 | Cite as

Compressive Sensing Based Soft Video Broadcast Using Spatial and Temporal Sparsity

  • Wenbin Yin
  • Xiaopeng Fan
  • Yunhui Shi
  • Ruiqin Xiong
  • Debin Zhao
Article

Abstract

Video broadcasting over wireless network has become a very popular application. However, the conventional digital video broadcasting framework can hardly accommodate heterogeneous users with diverse channel conditions, which is called the cliff effects. To overcome this cliff effects and provide a graceful degradation to multi-receivers, in this paper, we use the nonlocal sparsity and hierarchical GOP structure to propose a novel CS based soft video broadcast scheme. CS has properties of minimizing bandwidth consumption and generating measurements with equal importance which are exactly needed by video soft broadcast. In the proposed scheme, the measurement data are generated by block-wise compressive sensing (BCS), and then the measurement data packets are sent over a highly dense constellation though OFDM channel to achieve a simple encoder. Ideally, with the GOP structure, inter frame has lower sampling rate than intra frame to achieve better compression efficiency. At the decoder side, due to equally-important packets and property of soft broadcast, each user can receive the noise-corrupted measurements matching its channel condition and reconstruct video. The hierarchical GOP structure is presented to explode the correlation and non-local sparsity among video frames during the recover process. Additionally, using non-local sparsity, group based CS reconstruction with adaptive dictionaries is proposed to improve decoding quality. The experimental results show that the proposed scheme provides better performance compared with the traditional SoftCast with up to 8 dB coding gain for some channel conditions.

Keywords

Compressive sensing Video broadcast SoftCast Wireless network 

Notes

Acknowledgments

This work was supported in part by the National Science Foundation of China (NSFC) under grants 61472101 and 61390513, the Major State Basic Research Development Program of China (973 Program 2015CB351804), and the National High Technology Research and Development Program of China (863 Program 2015AA015903).

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Wenbin Yin
    • 1
  • Xiaopeng Fan
    • 1
  • Yunhui Shi
    • 2
  • Ruiqin Xiong
    • 3
  • Debin Zhao
    • 1
  1. 1.School of Computer Science & TechnologyHarbin Institute of TechnologyHarbinChina
  2. 2.College of Metropolitan TransportationBeijing University of TechnologyBeijingChina
  3. 3.Department of EECSPeking UniversityBeijingChina

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