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Figure 3a.
Figure 3b.
Video quality index dependency on the video content for various resolutions.
Take away The evolution of mobile network technologies, the sophistication of smartphone capabilities and the complexity of video streaming technologies enable consistent customer demand for high-quality OTT video streaming applications. Supporting OTT video streaming applications with seamless user experience becomes a significant challenge for operators due to the applications’ variety and diversity, as well as their lack of transparency for testing. All this must then be correlated against a backdrop of increased complexity in the mobile access technologies and under OPEX constraints. Since the performance of the OTT applications is showing continuous improvement while at the same time being something that operators cannot control and/or manage, a pragmatic testing approach is the most cost-efficient solution to address this task. Infovista developed such a solution based on a generic framework for testing a variety of native OTT applications with a common set of KPIs describing user-perceived waiting time, retainability and video quality during playback. Infovista empowers operators with a generic tool which enables consistency of testing across various native OTT applications. The benefit of network- centric VSQI, with calibration to the most sensitive video content and dual scoring, instantaneous and per session, is two-sided: troubleshooting accurately with fine resolution suited to drive testing and benchmarking the overall OTT video streaming session quality
Figure 3b shows a time snapshot of VSQIsession and MOS target values. The chart shows the raw VSQIsession values, as they would be displayed in field measurements, along with VSQIsession values after the 3rd-order polynomial mapping and MOS target values during the same time window. The chart shows that the 3rd-order polynomial values not only exhibit the same time distribution but also values very close to the MOS target values. This is further proven by the performance statistics presented above (Figure 3a). The raw VSQIsession values show the same time distribution, as expected, but the displayed values are slightly lower than for MOS target values. This behavior is intended by design for two main reasons. First, as mentioned in section “ Video Streaming Quality Index (VSQI) ”, to make the scoring independent of the content and to ensure sensitivity to network problems for easy detection, troubleshooting and optimization, a normalization to the most sensitive video content has been applied to VSQI model. Second, it is expected that higher video resolutions and frame rates will emerge with 5G Advanced. Thus, better video quality is expected from higher video resolutions and frame rates, and it should be possible to rank it against previous video quality on the same MOS scale. The VSQI calibration to MOS scale is designed to support large ranges of frame rates and resolutions up to 8K. Therefore, it is shown that VSQI provides the network-centric video quality with an accuracy characteristic of QoE video quality models and, unlike those models, supports high resolutions and frame rates.
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