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Demo: ASVTuw: Adaptive Scalable Video Transmission in Underwater Acoustic Networks

Published:29 December 2022Publication History

ABSTRACT

We propose an adaptive cross-layering solution for video transmissions in underwater acoustic multicast networks, namely Adaptive Scalable Video Transmission (ASVTuw). The ASVTuw customizes modulation and coding scheme and encodes videos with scalable video coding adaptively. In this live demo, we will show how the ASVTus works by conducting interactive water tank experiments and real-time MATLAB processing to reinforce our experiments and tune various key video transmission parameters.

References

  1. Divya Arora, Mehak Garg, and Megha Gupta. 2020. Diving Deep in Deep Convolutional Neural Network. In The 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN). 749–751.Google ScholarGoogle Scholar
  2. Mickael Raulet Mederic Blestel. 2010. Open SVC Decoder: A Flexible SVC Library. In Proceedings of the inter-national conference on Multimedia. 1463–1466.Google ScholarGoogle Scholar
  3. Zhuoran Qi, Roberto Petroccia, and Dario Pompili. 2022. ASVTuw: Adaptive Scalable Video Transmission in Underwater Acoustic Multicast Networks. In Proceedings of the International Conference on Underwater Networks Systems. 1–8.Google ScholarGoogle ScholarDigital LibraryDigital Library

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  • Published in

    cover image ACM Other conferences
    WUWNet '22: Proceedings of the 16th International Conference on Underwater Networks & Systems
    November 2022
    190 pages
    ISBN:9781450399524
    DOI:10.1145/3567600

    Copyright © 2022 Owner/Author

    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 29 December 2022

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    • Refereed limited

    Acceptance Rates

    Overall Acceptance Rate84of180submissions,47%
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