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Adaptive multi-view video streaming using side information over peer to peer networks

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journal contribution
posted on 02.08.2018, 15:22 authored by Cagri Ozcinar, Erhan EkmekciogluErhan Ekmekcioglu, Gholamreza Anbarjafari, Ahmet Kondoz
Multi-view plus-depth-map (MVD) video streaming with autostereoscopic displays provides multi-user immersive media experiences. In this context, delivery of MVD representation to multiple clients remains a challenging problem because of the high-volume of data involved and the inherent limitations imposed by the delivery networks. To this end, this paper investigates the side information (SI) assisted adaptation algorithm using peer-to-peer (P2P) systems. P2P delivery systems for MVD video can maximize link utilization, preventing the transport of multiple video copies of the same packet for many users. However, the quality of experience (QoE) can be significantly degraded by dynamic variations caused by network congestions. To this end, our solution comprises the extraction of low-overhead metadata at the encoding server that is distributed through the P2P network as SI and used by P2P clients performing network adaptation. In the proposed adaptation strategy, pre-selected views are discarded at times of network congestion and reconstructed with an optimal reconstruction performance using the delivered SI and the delivered neighboring camera views. The experimental results show that the robustness of P2P multi-view streaming using the proposed adaptation scheme is significantly increased in the P2P network.

History

School

  • Loughborough University London

Published in

Multimedia Tools and Applications

Volume

78

Issue

6

Pages

7225-7242

Citation

OZCINAR, C. ... et al, 2018. Adaptive multi-view video streaming using side information over peer to peer networks. Multimedia Tools and Applications, 78 (6), pp.7225–7242.

Publisher

© Springer Verlag

Version

AM (Accepted Manuscript)

Publisher statement

This is a post-peer-review, pre-copyedit version of an article published in Multimedia Tools and Applications. The final authenticated version is available online at: https://doi.org/10.1007/s11042-018-6492-5

Acceptance date

02/08/2018

Publication date

2018-08-08

ISSN

1380-7501

eISSN

1573-7721

Language

en