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QoS optimal real-time video streaming in distributed wireless image-sensing platforms

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Abstract

This paper provides a novel real-time video streaming method in distributed wireless image-sensing platforms. It consists of (1) a millimeter-wave (mmW)-based multi-hop routing optimization for real-time video streaming, (2) wireless image-sensing platforms by using the high-efficiency video coding. A mmW wireless communication is a promising technology for increasing capacity in next-generation wireless systems. However, the weakness of mmW signals to (1) do long-distance transmission and (2) survive in non-line-of-sight environments makes the mmW networks need a multi-hop relaying. Thus, this paper focuses on the maximization of video transmission quality of service (QoS) that makes the optimization problem different from the conventional sum-rate maximization. Specifically, this paper develops an algorithm that optimizes the summation of QoS of the individual wireless transmission of different video streams, subject to constraints of separate streams (i.e., minimum requirements in each stream). Experimental results show the proposed approach presents 32 % better performance with 1.84 dB gain in Y-peak signal-to-noise ratio than the widely used max–min flow routing that is generally considered in QoS-sensitive video streaming applications.

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Acknowledgments

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) and funded by the Ministry of Science, ICT & Future Planning (NRF-2015R1C1A1A02037743 and 2016R1C1B1015406).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Chung-Ang University, Heukseok-Ro 84, Dongjak-Ku, Seoul, 156-756, Korea

    Joongheon Kim

  2. Department of Computer Engineering, Gachon University, 1342 Seongnamdaero, Seongnam, Gyeonggi, 461-701, Korea

    Eun-Seok Ryu

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  1. Joongheon Kim
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Correspondence to Eun-Seok Ryu.

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Kim, J., Ryu, ES. QoS optimal real-time video streaming in distributed wireless image-sensing platforms. J Real-Time Image Proc 13, 547–556 (2017). https://doi.org/10.1007/s11554-016-0629-4

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  • DOI: https://doi.org/10.1007/s11554-016-0629-4

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