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The Impact of Packet Loss and Google Congestion Control on QoE for WebRTC-Based Mobile Multiparty Audiovisual Telemeetings

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MultiMedia Modeling (MMM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11295))

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Abstract

While previous expensive and complex desktop video conferencing solutions had a restricted reach, the emergence of the WebRTC (Web Real-Time Communication) open framework has provided an opportunity to redefine the video conferencing communication landscape. In particular, technological advances in terms of high resolution displays, cameras, and high speed wireless access networks have set the ground for emerging multiparty video telemeeting solutions realized via mobile devices. However, deploying multiparty video communication solutions on smart phones calls for the need to optimize video encoding parameters due to limited device processing power and dynamic wireless network conditions. In this paper, we report on a subjective user study involving 30 participants taking part in three-party audiovisual telemeetings on mobile devices. We conduct an experimental investigation of the Google Congestion Control (GCC) Algorithm in light of packet loss and under various video codec configurations, with the aim being to observe the impact on end user Quality of Experience (QoE). Results provide insights related to QoE-driven video encoding adaptation (in terms of bit rate, resolution, and frame rate), and show that in certain cases, adaptation invoked by GCC leads to video interruption. In majority of other cases, we observed that it took approximately 25 s for the video stream to recover to an acceptable quality level after the temporary occurrence of network packet loss.

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Notes

  1. 1.

    http://lynckia.com/licode/.

  2. 2.

    http://www.albedotelecom.com/pages/fieldtools/src/netstorm.php.

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Correspondence to Dunja Vucic .

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Vucic, D., Skorin-Kapov, L. (2019). The Impact of Packet Loss and Google Congestion Control on QoE for WebRTC-Based Mobile Multiparty Audiovisual Telemeetings. In: Kompatsiaris, I., Huet, B., Mezaris, V., Gurrin, C., Cheng, WH., Vrochidis, S. (eds) MultiMedia Modeling. MMM 2019. Lecture Notes in Computer Science(), vol 11295. Springer, Cham. https://doi.org/10.1007/978-3-030-05710-7_38

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  • DOI: https://doi.org/10.1007/978-3-030-05710-7_38

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  • Online ISBN: 978-3-030-05710-7

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