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  • 1.
    Fiedler, Markus
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för teknik och estetik.
    Zepernick, Hans-Juergen
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Kelkkanen, Viktor
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för teknik och estetik.
    Network-induced temporal disturbances in virtual reality applications2019Inngår i: 2019 11th International Conference on Quality of Multimedia Experience, QoMEX 2019, Institute of Electrical and Electronics Engineers Inc. , 2019Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Virtual Reality (VR) applications put high demands on software and hardware in order to enable an immersive experience for the user and avoid causing simulator sickness. As soon as networks become part of the Motion-To-Photon (MTP) path between rendering and display, there is a risk for extraordinary delays that may impair Quality of Experience (QoE). This short paper provides an overview of latency measurements and models that are applicable to the MTP path, complemented by demands on user and network levels. It specifically reports on freeze duration measurements using a commercial TPCAST wireless VR solution, and identifies a corresponding stochastic model of the freeze length distribution, which may serve as disturbance model for VR QoE studies. © 2019 IEEE.

  • 2.
    Kelkkanen, Viktor
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för teknik och estetik.
    Fiedler, Markus
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för teknik och estetik.
    Coefficient of Throughput Variation as Indication of Playback Freezes in Streamed Omnidirectional Videos2018Inngår i: 2018 28TH INTERNATIONAL TELECOMMUNICATION NETWORKS AND APPLICATIONS CONFERENCE (ITNAC), IEEE , 2018, s. 392-397Konferansepaper (Fagfellevurdert)
    Abstract [en]

    A large portion of today's network traffic consists of streamed video of large variety, such as films, television shows, live-streamed games and recently omnidirectional videos. A common way of delivering video is by using Dynamic Adaptive Streaming over HTTP (DASH), or recently with encrypted HTTPS. Encrypted video streams disable the use of Quality of Service (QoS) systems that rely on knowledge of application-dependent data, such as video resolution and bit-rate. This could make it difficult for a party providing bandwidth to efficiently allocate resources and estimate customer satisfaction. An application-independent way of measuring video stream quality could be of interest for such a party. In this paper, we investigate encrypted streaming of omni-directional video via YouTube to a smartphone in a Google Cardboard VR-headset. We monitored such sessions, delivered via both WiFi and mobile networks, at different times of day, implying different levels of congestion, and characterised the network traffic by using the Coefficient of Throughput Variation (CoTV) as statistic. We observe that this statistic shows to be able to indicate whether a stream is stable or unstable, in terms of potential video playback freezes, when the DASH delivery strategy is used.

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