Semantic Based Error Avoidance and Correction for Video Streaming

  • Christian Spielvogel
  • Sabina Serbu
  • Pascal Felber
  • Peter Kropf
Part of the Studies in Computational Intelligence book series (SCI, volume 279)


Video streaming over best effort networks remains a challenging task. Video quality decreases with an increasing number of frames that are corrupted, lost or only received after playback time. We use semantic information about the video and the network to decide between alternative or cooperative streaming sources to avoid or to correct data loss. We propose a distributed architecture that combines a peer-to-peer indexing archive for videos with error avoidance and error correction mechanisms to select the best delivery method from the corresponding sources. Our indexing-cache peer-to-peer overlay has two interesting properties for our selection model: it efficiently locates several sources for a video (if they exist) and even rare videos. Based on the coding characteristics of the available videos and the state of the network we apply a model for selecting between error avoidance, error correction and a combination of both approaches. This model is evaluated by using the network simulator NS-2 and a modified version of EvalVid.


Random Walk Packet Loss Video Stream Video Streaming Forward Error Correction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Lamparter, B., Boehrer, O., Effelsberg, W., Turau, V.: Adaptable forward error correction for multimedia data streams. Technical Report TR-93-009, University of Mannheim (1993)Google Scholar
  2. 2.
    Liu, H., Ma, H., Zarki, M.E., Gupta, S.: Error control schemes for networks: An Overview. Mobile Networks and Applications 2(2), 167–182 (1997)CrossRefGoogle Scholar
  3. 3.
    Lee, I., Guan, L.: Reliable video communication with multi-path streaming using mdc. In: IEEE International Conference on Multimedia and Expo, ICME 2005 (2005)Google Scholar
  4. 4.
    Boyce, J.M., Gaglianello, R.D.: Packet loss effects on mpeg video sent over the public internet. In: Multimedia 1998: Proceedings of the sixth ACM international conference on Multimedia, pp. 181–190. ACM Press, New York (1998)CrossRefGoogle Scholar
  5. 5.
    Klaue, J., Rathke, B., Wolisz, A.: Evalvid - a framework for video transmission and quality evaluation. In: Computer Performance Evaluation/Tools, pp. 255–272 (2003)Google Scholar
  6. 6.
    Padhye, J., Firoiu, V., Towsley, D., Kurose, J.: Modeling TCP throughput: A simple Model and its Empirical Validation. In: SIGCOMM 1998: Proceedings of the ACM SIGCOMM 1998 conference Applications, Technologies, Architectures and Protocols for Computer Communication, pp. 303–314. ACM Press, New York (1998)CrossRefGoogle Scholar
  7. 7.
    Park, K., Wang, W.: Qos-sensitive transport of real-time MPEG video using adaptive forward error correction. In: IEEE International Conference on Multimedia Computing and Systems, vol. 2, pp. 426–432 (1999)Google Scholar
  8. 8.
    Claypool, M., Zhu, Y.: Using interleaving to ameliorate the effects of packet loss in a video stream. In: ICDCSW 2003: Proceedings of the 23rd International Conference on Distributed Computing Systems, Washington, DC, USA, p. 508. IEEE Computer Society, Los Alamitos (2003)Google Scholar
  9. 9.
    Maxemchuk, N.F.: Dispersity Routing in Store-and-Forward Networks. PhD thesis, University of Pennsylvania (1975)Google Scholar
  10. 10.
    The Network Simulator NS-2 (v2.1b8a) (October 2001),

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Christian Spielvogel
    • 1
  • Sabina Serbu
    • 1
  • Pascal Felber
    • 1
  • Peter Kropf
    • 1
  1. 1.University of NeuchâtelSwitzerland

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