Unsupervised QoE field study for mobile YouTube video streaming with YoMoApp

Seufert, Michael; Wehner, Nikolas; Wamser, Florian; Casas, Pedro; D’Alconzo, Alessandro; Tran-Gia, Phuoc

doi:10.1109/qomex.2017.7965688

Cited by 22 publications

(16 citation statements)

References 19 publications

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“…YoMoApp (YouTube Monitoring App) [145] is an under improvements tool. It monitors the application and the network layer (i.e., the total amount of uploaded and downloaded data, is logged periodically) for both mobile and WiFi networks streaming parameters.…”

Section: Speechmentioning

confidence: 99%

Quality of Experience for Streaming Services: Measurements, Challenges and Insights

et al. 2020

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Over the last few years, the evolution of network and user handsets' technologies, have challenged the telecom industry and the Internet ecosystem. Especially, the unprecedented progress of multimedia streaming services like YouTube, Vimeo and DailyMotion resulted in an impressive demand growth and a significant need of Quality of Service (QoS) (e.g., high data rate, low latency/jitter, etc.). Mainly, numerous difficulties are to be considered while delivering a specific service, such as a strict QoS, human-centric features, massive number of devices, heterogeneous devices and networks, and uncontrollable environments. Thenceforth, the concept of Quality of Experience (QoE) is gaining visibility, and tremendous research efforts have been spent on improving and/or delivering reliable and addedvalue services, at a high user experience. In this paper, we present the importance of QoE in wireless and mobile networks (4G, 5G, and beyond), by providing standard definitions and the most important measurement methods developed. Moreover, we exhibit notable enhancements and controlling approaches proposed by researchers to meet the user expectation in terms of service experience.

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Section: Speechmentioning

confidence: 99%

Quality of Experience for Streaming Services: Measurements, Challenges and Insights

et al. 2020

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“…The impact of QoE on user engagement and the prediction of user engagement are also widely investigated research topics [5,9,17,28,29]. These studies show that especially the visual quality and stalling strongly impact the abandonment rate.…”

Section: Background and Related Workmentioning

confidence: 99%

Scoring High

Wehner

Seufert

Egger-Lampl

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

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Large-scale events pose severe challenges to live video streaming service providers, who need to cope with high, peaking viewer numbers and the resulting fluctuating resource demands, keeping high levels of Quality of Experience (QoE) to avoid end-user frustration and churn. In this paper, we analyze a unique dataset consisting of more than a million 2018 FIFA World Cup mobile live streaming sessions, collected at a large national public broadcaster. Different from previous work, we analyze QoE and user engagement as well as their interaction, in dependency to specific soccer match events, which have the potential to trigger flash crowds during a match. Flash crowds are a particular challenge to video service providers, since they cause sudden load peaks and consequently, the likelihood of quality problems. We further exploit the data to model viewer engagement over the course of a soccer match, and show that client counts follow very similar patterns of change across all matches. We believe that the analysis as well as the resulting models are valuable sources of insight for service providers, equipping them with tools for customer-centric resource and capacity management. CCS CONCEPTS • Networks → Network performance analysis; Network measurement; • Computing methodologies → Classification and regression trees.

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“…For example, authors in [10] use TCP level flow features collected from a mobile core network for developing stall detection models for HTTP video streaming, while authors in [11] and [12] develop mobile applications for inferring QoE from network measurements made on the end users' mobile devices. Considering mobile YouTube video, authors in [13] develop a mobile app for monitoring YouTube video streaming QoE where they collect passive measurements on the application and the network layers to infer the relationship between QoS and QoE for YouTube. These works use data collected in the wild.…”

Section: Related Workmentioning

confidence: 99%

From Network Traffic Measurements to QoE for Internet Video

Khokhar¹,

Ehlinger²,

Barakat³

2019

2019 IFIP Networking Conference (IFIP Networking)

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Video streaming is a dominant contributor to the global Internet traffic. Consequently, monitoring video streaming Quality of Experience (QoE) is of paramount importance to network providers. Monitoring QoE of video is a challenge as most of the video traffic of today is encrypted. In this paper, we consider this challenge and present an approach based on controlled experimentation and machine learning to estimate QoE from encrypted video traces using network level measurements only. We consider a case of YouTube and play out a wide range of videos under realistic network conditions to build ML models (classification and regression) that predict the subjective MOS (Mean Opinion Score) based on the ITU P.1203 model along with the QoE metrics of startup delay, quality (spatial resolution) of playout and quality variations, and this is using only the underlying network Quality of Service (QoS) features. We comprehensively evaluate our approach with different sets of input network features and output QoE metrics. Overall, our classification models predict the QoE metrics and the ITU MOS with an accuracy of 63-90% while the regression models show low error; the ITU MOS (1-5) and the startup delay (in seconds) are predicted with a root mean square error of 0.33 and 2.66 respectively.

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Unsupervised QoE field study for mobile YouTube video streaming with YoMoApp

Cited by 22 publications

References 19 publications

Quality of Experience for Streaming Services: Measurements, Challenges and Insights

Quality of Experience for Streaming Services: Measurements, Challenges and Insights

Scoring High

From Network Traffic Measurements to QoE for Internet Video

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