YouTube Video Streaming

Research Area

Research Area: YouTube Video Streaming

We deal within the CAN group with the research field "YouTube Video Streaming".

YouTube is one of the most popular services in today’s Internet. It has more than 1 billion users and every day people watch hundreds of millions of hours of YouTube videos. Half of those YouTube views are on mobile devices. On the one hand, (mobile) operators want to handle the huge amount of video traffic as efficiently as possible (high revenue per bit), on the other hand, they want to deliver a high Quality of Experience (QoE) to satisfy their customers. Therefore, it is very important for operators to understand the performance of their networks with respect to YouTube traffic.

We investigate the properties of the video streaming.
We offer tools for the analysis of the play behavior and define QoE models.

Best Paper Award auf der CNSM 2018

Mit ihrem Paper "A Fair Share for All: Novel Adaptation Logic for QoE Fairness of HTTP Adaptive Video Streaming" haben Michael Seufert, Nikolas Wehner, Pedro Casas und Florian Wamser den Best Paper Award auf der International Conference on Network and Service Management (CNSM 2018) in Rom gewonnen.

Mehr

Best Demonstration Award auf der TMA 2018

Florian Wamser, Nikolas Wehner, Michael Seufert, Pedro Casas und Phuoc Tran-Gia haben mit der Demo "YouTube QoE Monitoring with YoMoApp: A Web-based Data Interface for Researchers" den Best Demonstration Award auf der Network Traffic Measurement and Analysis Conference (TMA 2018) in Wien gewonnen.

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Demo @ IEEE/IFIP NOMS 2018: QoE Monitoring of the Native Android YouTube App

We'll show our Wrapper App for YouTube measurements of the native Google YouTube app as a live demonstration at the IEEE/IFIP NOMS 2018 conference (IEEE/IFIP Network Operations & Management Symposium). Visit us at Wednesday April 25th.

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Selected Findings and Results

Modeling the operation and traffic of YouTube:

Modeling the YouTube Stack: from Packets to Quality of Experience. Wamser, Florian; Casas, Pedro; Seufert, Michael; Moldovan, Christian; Tran-Gia, Phuoc; Hoßfeld, Tobias. In Computer Networks, 109(2), pp. 211–224. 2016.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
- [ Download ]
YouTube is one of the most popular and volume-dominant services in today’s Internet, and has changed the Web for ever. Consequently, network operators are forced to consider it in the design, deployment, and optimization of their networks. Taming YouTube requires a good understanding of the complete YouTube stack, from the network streaming service to the application itself. Understanding the interplays between individual YouTube functionalities and their implications for traffic and user Quality of Experience (QoE) becomes paramount nowadays. In this paper we characterize and model the YouTube stack at different layers, going from the generated network traffic to the QoE perceived by the users watching YouTube videos. Firstly, we present a network traffic model for the YouTube flow control mechanism, which permits to understand how YouTube provisions video traffic flows to users. Secondly, we investigate how traffic is consumed at the client side, deriving a simple model for the YouTube application. Thirdly, we analyze the implications for the end user, and present a model for the quality as perceived by them. This model is finally integrated into a system for real time QoE-based YouTube monitoring, highly useful to operators to assess the performance of their networks for provisioning YouTube videos. The central parameter for all the presented models is the buffer level at the YouTube application layer. This paper provides an extensive compendium of objective tools and models for network operators to better understand the YouTube traffic in their networks, to predict the playback behavior of the video player, and to assess how well they do it in the practice with the satisfaction of their customers watching YouTube.

@article{info3-article-2016-6, abstract = {YouTube is one of the most popular and volume-dominant services in today’s Internet, and has changed the Web for ever. Consequently, network operators are forced to consider it in the design, deployment, and optimization of their networks. Taming YouTube requires a good understanding of the complete YouTube stack, from the network streaming service to the application itself. Understanding the interplays between individual YouTube functionalities and their implications for traffic and user Quality of Experience (QoE) becomes paramount nowadays. In this paper we characterize and model the YouTube stack at different layers, going from the generated network traffic to the QoE perceived by the users watching YouTube videos. Firstly, we present a network traffic model for the YouTube flow control mechanism, which permits to understand how YouTube provisions video traffic flows to users. Secondly, we investigate how traffic is consumed at the client side, deriving a simple model for the YouTube application. Thirdly, we analyze the implications for the end user, and present a model for the quality as perceived by them. This model is finally integrated into a system for real time QoE-based YouTube monitoring, highly useful to operators to assess the performance of their networks for provisioning YouTube videos. The central parameter for all the presented models is the buffer level at the YouTube application layer. This paper provides an extensive compendium of objective tools and models for network operators to better understand the YouTube traffic in their networks, to predict the playback behavior of the video player, and to assess how well they do it in the practice with the satisfaction of their customers watching YouTube.}, author = {Wamser, Florian and Casas, Pedro and Seufert, Michael and Moldovan, Christian and Tran-Gia, Phuoc and Hoßfeld, Tobias}, journal = {Computer Networks}, keywords = {can}, month = 11, number = 2, pages = {211-224}, title = {Modeling the YouTube Stack: from Packets to Quality of Experience}, volume = 109, year = 2016 }

%0 Journal Article %1 info3-article-2016-6 %A Wamser, Florian %A Casas, Pedro %A Seufert, Michael %A Moldovan, Christian %A Tran-Gia, Phuoc %A Hoßfeld, Tobias %D 2016 %J Computer Networks %N 2 %P 211-224 %T Modeling the YouTube Stack: from Packets to Quality of Experience %V 109 %X YouTube is one of the most popular and volume-dominant services in today’s Internet, and has changed the Web for ever. Consequently, network operators are forced to consider it in the design, deployment, and optimization of their networks. Taming YouTube requires a good understanding of the complete YouTube stack, from the network streaming service to the application itself. Understanding the interplays between individual YouTube functionalities and their implications for traffic and user Quality of Experience (QoE) becomes paramount nowadays. In this paper we characterize and model the YouTube stack at different layers, going from the generated network traffic to the QoE perceived by the users watching YouTube videos. Firstly, we present a network traffic model for the YouTube flow control mechanism, which permits to understand how YouTube provisions video traffic flows to users. Secondly, we investigate how traffic is consumed at the client side, deriving a simple model for the YouTube application. Thirdly, we analyze the implications for the end user, and present a model for the quality as perceived by them. This model is finally integrated into a system for real time QoE-based YouTube monitoring, highly useful to operators to assess the performance of their networks for provisioning YouTube videos. The central parameter for all the presented models is the buffer level at the YouTube application layer. This paper provides an extensive compendium of objective tools and models for network operators to better understand the YouTube traffic in their networks, to predict the playback behavior of the video player, and to assess how well they do it in the practice with the satisfaction of their customers watching YouTube.

Software Tool to evaluate YouTube QoE for mobile networks:

YoMo - Wrapper App
A Wrapper App for YouTube's Native App

Poster (PDF) | Publications | Contact
http://github.com/lsinfo3/yomo-wrapperapp

YoMoApp
YouTube Performance Monitoring Application

YoMoApp: a Tool for Analyzing QoE of YouTube HTTP Adaptive Streaming in Mobile Networks. Wamser, Florian; Seufert, Michael; Casas, Pedro; Irmer, Ralf; Tran-Gia, Phuoc; Schatz, Raimund. In European Conference on Networks and Communications (EuCNC). Paris, France, 2015.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
The performance of YouTube in mobile networks is crucial to network operators, who try to find a trade-off between cost-efficient handling of the huge traffic amounts and high perceived end-user Quality of Experience (QoE). This paper introduces YoMoApp (YouTube Performance Monitoring Application), an Android application, which passively monitors key performance indicators (KPIs) of YouTube adaptive video streaming on end-user smartphones. The monitored KPIs (i.e., player state/events, buffer, and video quality level) can be used to analyze the QoE of mobile YouTube video sessions. YoMoApp is a valuable tool to assess the performance of mobile networks with respect to YouTube traffic, as well as to develop optimizations and QoE models for mobile HTTP adaptive streaming. We test YoMoApp through real subjective QoE tests showing that the tool is accurate to capture the experience of end-users watching YouTube on smartphones.

@inproceedings{info3-inproceedings-2015-513, abstract = {The performance of YouTube in mobile networks is crucial to network operators, who try to find a trade-off between cost-efficient handling of the huge traffic amounts and high perceived end-user Quality of Experience (QoE). This paper introduces YoMoApp (YouTube Performance Monitoring Application), an Android application, which passively monitors key performance indicators (KPIs) of YouTube adaptive video streaming on end-user smartphones. The monitored KPIs (i.e., player state/events, buffer, and video quality level) can be used to analyze the QoE of mobile YouTube video sessions. YoMoApp is a valuable tool to assess the performance of mobile networks with respect to YouTube traffic, as well as to develop optimizations and QoE models for mobile HTTP adaptive streaming. We test YoMoApp through real subjective QoE tests showing that the tool is accurate to capture the experience of end-users watching YouTube on smartphones.}, address = {Paris, France}, author = {Wamser, Florian and Seufert, Michael and Casas, Pedro and Irmer, Ralf and Tran-Gia, Phuoc and Schatz, Raimund}, booktitle = {European Conference on Networks and Communications (EuCNC)}, keywords = {demobenefit}, month = {06}, title = {YoMoApp: a Tool for Analyzing QoE of YouTube HTTP Adaptive Streaming in Mobile Networks}, year = 2015 }

%0 Conference Paper %1 info3-inproceedings-2015-513 %A Wamser, Florian %A Seufert, Michael %A Casas, Pedro %A Irmer, Ralf %A Tran-Gia, Phuoc %A Schatz, Raimund %B European Conference on Networks and Communications (EuCNC) %C Paris, France %D 2015 %T YoMoApp: a Tool for Analyzing QoE of YouTube HTTP Adaptive Streaming in Mobile Networks %X The performance of YouTube in mobile networks is crucial to network operators, who try to find a trade-off between cost-efficient handling of the huge traffic amounts and high perceived end-user Quality of Experience (QoE). This paper introduces YoMoApp (YouTube Performance Monitoring Application), an Android application, which passively monitors key performance indicators (KPIs) of YouTube adaptive video streaming on end-user smartphones. The monitored KPIs (i.e., player state/events, buffer, and video quality level) can be used to analyze the QoE of mobile YouTube video sessions. YoMoApp is a valuable tool to assess the performance of mobile networks with respect to YouTube traffic, as well as to develop optimizations and QoE models for mobile HTTP adaptive streaming. We test YoMoApp through real subjective QoE tests showing that the tool is accurate to capture the experience of end-users watching YouTube on smartphones.

Publications

2021[ to top ]

Uplink vs. Downlink: Machine Learning-Based Quality Prediction for HTTP Adaptive Video Streaming. Loh, Frank; Poignée, Fabian; Wamser, Florian; Leidinger, Ferdinand; Hoßfeld, Tobias. In Sensors, 21(12), p. 4172. 2021.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
Streaming video is responsible for the bulk of Internet traffic these days. For this reason, Internet providers and network operators try to make predictions and assessments about the streaming quality for an end user. Current monitoring solutions are based on a variety of different machine learning approaches. The challenge for providers and operators nowadays is that existing approaches require large amounts of data. In this work, the most relevant quality of experience metrics, i.e., the initial playback delay, the video streaming quality, video quality changes, and video rebuffering events, are examined using a voluminous data set of more than 13,000 YouTube video streaming runs that were collected with the native YouTube mobile app. Three Machine Learning models are developed and compared to estimate playback behavior based on uplink request information. The main focus has been on developing a lightweight approach using as few features and as little data as possible, while maintaining state-of-the-art performance.

@article{info3-article-2021-3, abstract = {Streaming video is responsible for the bulk of Internet traffic these days. For this reason, Internet providers and network operators try to make predictions and assessments about the streaming quality for an end user. Current monitoring solutions are based on a variety of different machine learning approaches. The challenge for providers and operators nowadays is that existing approaches require large amounts of data. In this work, the most relevant quality of experience metrics, i.e., the initial playback delay, the video streaming quality, video quality changes, and video rebuffering events, are examined using a voluminous data set of more than 13,000 YouTube video streaming runs that were collected with the native YouTube mobile app. Three Machine Learning models are developed and compared to estimate playback behavior based on uplink request information. The main focus has been on developing a lightweight approach using as few features and as little data as possible, while maintaining state-of-the-art performance.}, author = {Loh, Frank and Poignée, Fabian and Wamser, Florian and Leidinger, Ferdinand and Hoßfeld, Tobias}, journal = {Sensors}, keywords = {can}, number = 12, pages = 4172, title = {Uplink vs. Downlink: Machine Learning-Based Quality Prediction for HTTP Adaptive Video Streaming}, volume = 21, year = 2021 }

%0 Journal Article %1 info3-article-2021-3 %A Loh, Frank %A Poignée, Fabian %A Wamser, Florian %A Leidinger, Ferdinand %A Hoßfeld, Tobias %D 2021 %J Sensors %N 12 %P 4172 %T Uplink vs. Downlink: Machine Learning-Based Quality Prediction for HTTP Adaptive Video Streaming %V 21 %X Streaming video is responsible for the bulk of Internet traffic these days. For this reason, Internet providers and network operators try to make predictions and assessments about the streaming quality for an end user. Current monitoring solutions are based on a variety of different machine learning approaches. The challenge for providers and operators nowadays is that existing approaches require large amounts of data. In this work, the most relevant quality of experience metrics, i.e., the initial playback delay, the video streaming quality, video quality changes, and video rebuffering events, are examined using a voluminous data set of more than 13,000 YouTube video streaming runs that were collected with the native YouTube mobile app. Three Machine Learning models are developed and compared to estimate playback behavior based on uplink request information. The main focus has been on developing a lightweight approach using as few features and as little data as possible, while maintaining state-of-the-art performance.

2019[ to top ]

To Share or Not to Share? How Exploitation of Context Data Can Improve In-Network QoE Monitoring of Encrypted YouTube Streams. Orsolic, Irena; Skorin-Kapov, Lea; Hoßfeld, Tobias. In 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX) (QoMEX 2019). 2019.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
With the widespread use of encryption in Over the Top (OTT) traffic, Internet Service Providers (ISPs) for the most part lack insights into application performance, as well as into Quality of Experience (QoE) perceived by end users. Addressing challenges related to encryption, ISPs are looking into machine learning based solutions that can detect application performance solely from statistical properties of the traffic. On the other hand, OTT service providers are not willing to share service performance and content-related information with ISPs. While related work on OTT-ISP collaboration scenarios has addressed architectural aspects, business models, and to a certain extent incentives for sharing data, the focus of this paper is on the exchanged data itself. We investigate to what extent the performance of in-network machine learning based QoE estimation models for HTTP adaptive video streaming could be improved with the availability of certain context data provided by OTT providers. We motivate OTT-ISP collaboration through more accurate in-network QoE monitoring and potential improvement of user experience, which is of interest to both sides.

@inproceedings{info3-article-2019-9, abstract = {With the widespread use of encryption in Over the Top (OTT) traffic, Internet Service Providers (ISPs) for the most part lack insights into application performance, as well as into Quality of Experience (QoE) perceived by end users. Addressing challenges related to encryption, ISPs are looking into machine learning based solutions that can detect application performance solely from statistical properties of the traffic. On the other hand, OTT service providers are not willing to share service performance and content-related information with ISPs. While related work on OTT-ISP collaboration scenarios has addressed architectural aspects, business models, and to a certain extent incentives for sharing data, the focus of this paper is on the exchanged data itself. We investigate to what extent the performance of in-network machine learning based QoE estimation models for HTTP adaptive video streaming could be improved with the availability of certain context data provided by OTT providers. We motivate OTT-ISP collaboration through more accurate in-network QoE monitoring and potential improvement of user experience, which is of interest to both sides.}, author = {Orsolic, Irena and Skorin-Kapov, Lea and Hoßfeld, Tobias}, booktitle = {2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX) (QoMEX 2019)}, keywords = {YouTube}, month = {06}, title = {To Share or Not to Share? How Exploitation of Context Data Can Improve In-Network QoE Monitoring of Encrypted YouTube Streams}, year = 2019 }

%0 Conference Paper %1 info3-article-2019-9 %A Orsolic, Irena %A Skorin-Kapov, Lea %A Hoßfeld, Tobias %B 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX) (QoMEX 2019) %D 2019 %T To Share or Not to Share? How Exploitation of Context Data Can Improve In-Network QoE Monitoring of Encrypted YouTube Streams %X With the widespread use of encryption in Over the Top (OTT) traffic, Internet Service Providers (ISPs) for the most part lack insights into application performance, as well as into Quality of Experience (QoE) perceived by end users. Addressing challenges related to encryption, ISPs are looking into machine learning based solutions that can detect application performance solely from statistical properties of the traffic. On the other hand, OTT service providers are not willing to share service performance and content-related information with ISPs. While related work on OTT-ISP collaboration scenarios has addressed architectural aspects, business models, and to a certain extent incentives for sharing data, the focus of this paper is on the exchanged data itself. We investigate to what extent the performance of in-network machine learning based QoE estimation models for HTTP adaptive video streaming could be improved with the availability of certain context data provided by OTT providers. We motivate OTT-ISP collaboration through more accurate in-network QoE monitoring and potential improvement of user experience, which is of interest to both sides.

2018[ to top ]

A Fair Share for All: Novel Adaptation Logic for QoE Fairness of HTTP Adaptive Video Streaming. Seufert, Michael; Wehner, Nikolas; Casas, Pedro; Wamser, Florian. In 14th International Conference on Network and Service Management (CNSM), Best Paper Award. Rome, Italy, 2018.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
This paper presents a novel adaptation logic for HTTP adaptive streaming (HAS), which achieves not only a high Quality of Experience (QoE) but also high QoE fairness among independent and heterogeneous clients. The algorithm forces video clients to adapt the requested quality level based on the current network conditions and their individual bit rate requirements, such that the overall quality levels selected by all currently active streaming clients are fairly distributed, i.e., they do not diverge too much. The design of the algorithm is inspired by the well-known Transmission Control Protocol (TCP) congestion control, and drives heterogeneous clients to independently converge on similar quality levels without the need for communicating with each other and/or with a centralized controller in the network. By defining quality levels with equal visual quality, and preparing video representations accordingly, the quality level fairness is extended to QoE fairness. In this work, the design of the algorithm is described and a simulative performance evaluation is conducted to compare the QoE and QoE fairness of the proposed algorithm with other HAS adaptation logics.

@inproceedings{info3-inproceedings-2018-38, abstract = {This paper presents a novel adaptation logic for HTTP adaptive streaming (HAS), which achieves not only a high Quality of Experience (QoE) but also high QoE fairness among independent and heterogeneous clients. The algorithm forces video clients to adapt the requested quality level based on the current network conditions and their individual bit rate requirements, such that the overall quality levels selected by all currently active streaming clients are fairly distributed, i.e., they do not diverge too much. The design of the algorithm is inspired by the well-known Transmission Control Protocol (TCP) congestion control, and drives heterogeneous clients to independently converge on similar quality levels without the need for communicating with each other and/or with a centralized controller in the network. By defining quality levels with equal visual quality, and preparing video representations accordingly, the quality level fairness is extended to QoE fairness. In this work, the design of the algorithm is described and a simulative performance evaluation is conducted to compare the QoE and QoE fairness of the proposed algorithm with other HAS adaptation logics.}, address = {Rome, Italy}, author = {Seufert, Michael and Wehner, Nikolas and Casas, Pedro and Wamser, Florian}, booktitle = {14th International Conference on Network and Service Management (CNSM), Best Paper Award}, keywords = {can}, month = 11, title = {A Fair Share for All: Novel Adaptation Logic for QoE Fairness of HTTP Adaptive Video Streaming}, year = 2018 }

%0 Conference Paper %1 info3-inproceedings-2018-38 %A Seufert, Michael %A Wehner, Nikolas %A Casas, Pedro %A Wamser, Florian %B 14th International Conference on Network and Service Management (CNSM), Best Paper Award %C Rome, Italy %D 2018 %T A Fair Share for All: Novel Adaptation Logic for QoE Fairness of HTTP Adaptive Video Streaming %X This paper presents a novel adaptation logic for HTTP adaptive streaming (HAS), which achieves not only a high Quality of Experience (QoE) but also high QoE fairness among independent and heterogeneous clients. The algorithm forces video clients to adapt the requested quality level based on the current network conditions and their individual bit rate requirements, such that the overall quality levels selected by all currently active streaming clients are fairly distributed, i.e., they do not diverge too much. The design of the algorithm is inspired by the well-known Transmission Control Protocol (TCP) congestion control, and drives heterogeneous clients to independently converge on similar quality levels without the need for communicating with each other and/or with a centralized controller in the network. By defining quality levels with equal visual quality, and preparing video representations accordingly, the quality level fairness is extended to QoE fairness. In this work, the design of the algorithm is described and a simulative performance evaluation is conducted to compare the QoE and QoE fairness of the proposed algorithm with other HAS adaptation logics.
Beauty is in the Eye of the Smartphone Holder - A Data Driven Analysis of YouTube Mobile QoE. Wehner, Nikolas; Wassermann, Sarah; Casas, Pedro; Seufert, Michael; Wamser, Florian. In 14th International Conference on Network and Service Management (CNSM). Rome, Italy, 2018.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
Measuring the Quality of Experience (QoE) undergone by cellular network users has become paramount for cellular ISPs. Given its overwhelming dominance and ever-growing popularity, this paper focuses on the analysis of QoE for YouTube in mobile networks. Using a large-scale dataset of crowdsourced YouTube QoE measurements collected in smartphones with YoMoApp, we analyze the evolution of multiple relevant QoE-related metrics over time for YouTube mobile users. The dataset includes measurements from more than 360 users worldwide, spanning over the last five years. Our data-driven analysis shows a systematic performance and QoE improvement of YouTube in mobile devices over time, accompanied by an improvement of cellular network performance and by an optimization of the YouTube streaming behavior for smartphones.

@inproceedings{info3-inproceedings-2018-39, abstract = {Measuring the Quality of Experience (QoE) undergone by cellular network users has become paramount for cellular ISPs. Given its overwhelming dominance and ever-growing popularity, this paper focuses on the analysis of QoE for YouTube in mobile networks. Using a large-scale dataset of crowdsourced YouTube QoE measurements collected in smartphones with YoMoApp, we analyze the evolution of multiple relevant QoE-related metrics over time for YouTube mobile users. The dataset includes measurements from more than 360 users worldwide, spanning over the last five years. Our data-driven analysis shows a systematic performance and QoE improvement of YouTube in mobile devices over time, accompanied by an improvement of cellular network performance and by an optimization of the YouTube streaming behavior for smartphones.}, address = {Rome, Italy}, author = {Wehner, Nikolas and Wassermann, Sarah and Casas, Pedro and Seufert, Michael and Wamser, Florian}, booktitle = {14th International Conference on Network and Service Management (CNSM)}, keywords = {can}, month = 11, title = {Beauty is in the Eye of the Smartphone Holder - A Data Driven Analysis of YouTube Mobile QoE}, year = 2018 }

%0 Conference Paper %1 info3-inproceedings-2018-39 %A Wehner, Nikolas %A Wassermann, Sarah %A Casas, Pedro %A Seufert, Michael %A Wamser, Florian %B 14th International Conference on Network and Service Management (CNSM) %C Rome, Italy %D 2018 %T Beauty is in the Eye of the Smartphone Holder - A Data Driven Analysis of YouTube Mobile QoE %X Measuring the Quality of Experience (QoE) undergone by cellular network users has become paramount for cellular ISPs. Given its overwhelming dominance and ever-growing popularity, this paper focuses on the analysis of QoE for YouTube in mobile networks. Using a large-scale dataset of crowdsourced YouTube QoE measurements collected in smartphones with YoMoApp, we analyze the evolution of multiple relevant QoE-related metrics over time for YouTube mobile users. The dataset includes measurements from more than 360 users worldwide, spanning over the last five years. Our data-driven analysis shows a systematic performance and QoE improvement of YouTube in mobile devices over time, accompanied by an improvement of cellular network performance and by an optimization of the YouTube streaming behavior for smartphones.
Impact of VNF Placements on QoE Monitoring in the Cloud. Dinh-Xuan, Lam; Popp, Christian; Burger, Valentin; Wamser, Florian; Hoßfeld, Tobias. In International Journal of Network Management, (NEM-18-0120.R1). 2018.
- [ BibTeX ]
- [ EndNote ]
@article{info3-article-2018-6, author = {Dinh-Xuan, Lam and Popp, Christian and Burger, Valentin and Wamser, Florian and Hoßfeld, Tobias}, journal = {International Journal of Network Management}, keywords = {video-quality}, month = 10, number = {NEM-18-0120.R1}, title = {Impact of VNF Placements on QoE Monitoring in the Cloud}, year = 2018 }

%0 Journal Article %1 info3-article-2018-6 %A Dinh-Xuan, Lam %A Popp, Christian %A Burger, Valentin %A Wamser, Florian %A Hoßfeld, Tobias %D 2018 %J International Journal of Network Management %N NEM-18-0120.R1 %T Impact of VNF Placements on QoE Monitoring in the Cloud
A Wrapper for Automatic Measurements with YouTube’s Native Android App. Seufert, Michael; Zeidler, Bernd; Wamser, Florian; Karagkioules, Theodoros; Tsilimantos, Dimitrios; Loh, Frank; Tran-Gia, Phuoc; Valentin, Stefan. In Network Traffic Measurement and Analysis Conference (TMA). Vienna, Austria, 2018.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
YouTube is one of the most popular and demanding services in the Internet today. Thereby, a large portion of this traffic is generated by YouTube's mobile app. While past studies have shown how to monitor browser-based streaming on desktop PCs (e.g., YoMo) or mobile devices (e.g., YoMoApp), streaming in the native app has not been monitored yet. This paper presents an automated framework for monitoring the streaming in YouTube's native app for Android. The concept is based on a wrapper application and the Android Debug Bridge (adb), and can be also extended to automatic measurements with other apps. For YouTube, it allows to collect application-layer streaming data, such as current playtime, buffered playtime, video encoding, and quality switches. These data can be complemented with network measurements on the mobile access link to obtain a holistic view on mobile YouTube streaming on Android devices. In addition to describing the software design and testbed setup, this paper discusses an experimental measurement. This study analyzes the streaming in the native YouTube app and compares it to the streaming from the mobile YouTube website via YoMoApp.

@inproceedings{info3-article-2018-4, abstract = {YouTube is one of the most popular and demanding services in the Internet today. Thereby, a large portion of this traffic is generated by YouTube's mobile app. While past studies have shown how to monitor browser-based streaming on desktop PCs (e.g., YoMo) or mobile devices (e.g., YoMoApp), streaming in the native app has not been monitored yet. This paper presents an automated framework for monitoring the streaming in YouTube's native app for Android. The concept is based on a wrapper application and the Android Debug Bridge (adb), and can be also extended to automatic measurements with other apps. For YouTube, it allows to collect application-layer streaming data, such as current playtime, buffered playtime, video encoding, and quality switches. These data can be complemented with network measurements on the mobile access link to obtain a holistic view on mobile YouTube streaming on Android devices. In addition to describing the software design and testbed setup, this paper discusses an experimental measurement. This study analyzes the streaming in the native YouTube app and compares it to the streaming from the mobile YouTube website via YoMoApp.}, address = {Vienna, Austria}, author = {Seufert, Michael and Zeidler, Bernd and Wamser, Florian and Karagkioules, Theodoros and Tsilimantos, Dimitrios and Loh, Frank and Tran-Gia, Phuoc and Valentin, Stefan}, booktitle = {Network Traffic Measurement and Analysis Conference (TMA)}, keywords = {can}, month = {04}, title = {A Wrapper for Automatic Measurements with YouTube's Native Android App}, year = 2018 }

%0 Conference Paper %1 info3-article-2018-4 %A Seufert, Michael %A Zeidler, Bernd %A Wamser, Florian %A Karagkioules, Theodoros %A Tsilimantos, Dimitrios %A Loh, Frank %A Tran-Gia, Phuoc %A Valentin, Stefan %B Network Traffic Measurement and Analysis Conference (TMA) %C Vienna, Austria %D 2018 %T A Wrapper for Automatic Measurements with YouTube's Native Android App %X YouTube is one of the most popular and demanding services in the Internet today. Thereby, a large portion of this traffic is generated by YouTube's mobile app. While past studies have shown how to monitor browser-based streaming on desktop PCs (e.g., YoMo) or mobile devices (e.g., YoMoApp), streaming in the native app has not been monitored yet. This paper presents an automated framework for monitoring the streaming in YouTube's native app for Android. The concept is based on a wrapper application and the Android Debug Bridge (adb), and can be also extended to automatic measurements with other apps. For YouTube, it allows to collect application-layer streaming data, such as current playtime, buffered playtime, video encoding, and quality switches. These data can be complemented with network measurements on the mobile access link to obtain a holistic view on mobile YouTube streaming on Android devices. In addition to describing the software design and testbed setup, this paper discusses an experimental measurement. This study analyzes the streaming in the native YouTube app and compares it to the streaming from the mobile YouTube website via YoMoApp.
A Wrapper for Automated Measurements with YouTube’s Native App. Loh, Frank; Karagkioules, Theodoros; Seufert, Michael; Zeidler, Bernd; Tsilimantos, Dimitrios; Tran-Gia, Phuoc; Valentin, Stefan; Wamser, Florian. Taipei, Taiwan, 2018, April.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
This demo introduces a wrapper used for automated measurements of mobile video streaming in the Android YouTube app. The difference to traditional measurement techniques is that the measurement is done with the native YouTube app as it is provided in the Google Play Store. In addition to bandwidth or packet loss detection, the QoE of the video stream can be measured and quantified. For this, the amount of quality changes, the current playtime, the buffer level, and statistics like video and audio format are captured. Thus, detailed relationships between network parameters and streaming behavior based on many factors can be detected within the native app available in the Play Store.

@misc{info3-demo-2018-1, abstract = {This demo introduces a wrapper used for automated measurements of mobile video streaming in the Android YouTube app. The difference to traditional measurement techniques is that the measurement is done with the native YouTube app as it is provided in the Google Play Store. In addition to bandwidth or packet loss detection, the QoE of the video stream can be measured and quantified. For this, the amount of quality changes, the current playtime, the buffer level, and statistics like video and audio format are captured. Thus, detailed relationships between network parameters and streaming behavior based on many factors can be detected within the native app available in the Play Store.}, address = {Taipei, Taiwan}, author = {Loh, Frank and Karagkioules, Theodoros and Seufert, Michael and Zeidler, Bernd and Tsilimantos, Dimitrios and Tran-Gia, Phuoc and Valentin, Stefan and Wamser, Florian}, booktitle = {IEEE/IFIP NOMS 2018}, keywords = {can}, month = {04}, title = {A Wrapper for Automated Measurements with YouTube’s Native App}, year = 2018 }

%0 Generic %1 info3-demo-2018-1 %A Loh, Frank %A Karagkioules, Theodoros %A Seufert, Michael %A Zeidler, Bernd %A Tsilimantos, Dimitrios %A Tran-Gia, Phuoc %A Valentin, Stefan %A Wamser, Florian %B IEEE/IFIP NOMS 2018 %C Taipei, Taiwan %D 2018 %T A Wrapper for Automated Measurements with YouTube’s Native App %X This demo introduces a wrapper used for automated measurements of mobile video streaming in the Android YouTube app. The difference to traditional measurement techniques is that the measurement is done with the native YouTube app as it is provided in the Google Play Store. In addition to bandwidth or packet loss detection, the QoE of the video stream can be measured and quantified. For this, the amount of quality changes, the current playtime, the buffer level, and statistics like video and audio format are captured. Thus, detailed relationships between network parameters and streaming behavior based on many factors can be detected within the native app available in the Play Store.

2017[ to top ]

YouTube QoE Monitoring with YoMoApp: a Mobile App for Crowdsourced YouTube QoE Analysis. Seufert, Michael; Wehner, Nikolas; Wamser, Florian; Casas, Pedro. London, UK, 2017, November.
- [ BibTeX ]
- [ EndNote ]
@misc{info3-poster-2017-1, address = {London, UK}, author = {Seufert, Michael and Wehner, Nikolas and Wamser, Florian and Casas, Pedro}, booktitle = {ACM Internet Measurement Conference (IMC)}, keywords = {can}, month = 11, title = {YouTube QoE Monitoring with YoMoApp: a Mobile App for Crowdsourced YouTube QoE Analysis}, year = 2017 }

%0 Generic %1 info3-poster-2017-1 %A Seufert, Michael %A Wehner, Nikolas %A Wamser, Florian %A Casas, Pedro %B ACM Internet Measurement Conference (IMC) %C London, UK %D 2017 %T YouTube QoE Monitoring with YoMoApp: a Mobile App for Crowdsourced YouTube QoE Analysis
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. In 9th International Conference on Quality of Multimedia Experience (QoMEX). Erfurt, Germany, 2017.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
YoMoApp (YouTube Monitoring App) is an Android app to monitor mobile YouTube video streaming on both application- and network-layer. Additionally, it allows to collect subjective Quality of Experience (QoE) feedback of end users. During the development of the app, the stable versions of YoMoApp were already available in the Google Play Store, and the app was downloaded, installed, and used on many devices to monitor streaming sessions. As the app was not advertised in special campaigns or used for dedicated QoE studies, the monitored streaming sessions of this period compose the data set of a large unsupervised field study. The collected data set is evaluated to characterize current mobile YouTube streaming on both application and network layers. Furthermore, the problems and methodology to obtain QoE results from such unsupervised field study are discussed together with the actual QoE results. Correlations between QoE factors are investigated, and the QoE of clusters of similar streaming sessions is analyzed.

@inproceedings{info3-inproceedings-2017-9, abstract = {YoMoApp (YouTube Monitoring App) is an Android app to monitor mobile YouTube video streaming on both application- and network-layer. Additionally, it allows to collect subjective Quality of Experience (QoE) feedback of end users. During the development of the app, the stable versions of YoMoApp were already available in the Google Play Store, and the app was downloaded, installed, and used on many devices to monitor streaming sessions. As the app was not advertised in special campaigns or used for dedicated QoE studies, the monitored streaming sessions of this period compose the data set of a large unsupervised field study. The collected data set is evaluated to characterize current mobile YouTube streaming on both application and network layers. Furthermore, the problems and methodology to obtain QoE results from such unsupervised field study are discussed together with the actual QoE results. Correlations between QoE factors are investigated, and the QoE of clusters of similar streaming sessions is analyzed.}, address = {Erfurt, Germany}, author = {Seufert, Michael and Wehner, Nikolas and Wamser, Florian and Casas, Pedro and D'Alconzo, Alessandro and Tran-Gia, Phuoc}, booktitle = {9th International Conference on Quality of Multimedia Experience (QoMEX)}, keywords = {can}, month = {05}, title = {Unsupervised QoE Field Study for Mobile YouTube Video Streaming with YoMoApp}, year = 2017 }

%0 Conference Paper %1 info3-inproceedings-2017-9 %A Seufert, Michael %A Wehner, Nikolas %A Wamser, Florian %A Casas, Pedro %A D'Alconzo, Alessandro %A Tran-Gia, Phuoc %B 9th International Conference on Quality of Multimedia Experience (QoMEX) %C Erfurt, Germany %D 2017 %T Unsupervised QoE Field Study for Mobile YouTube Video Streaming with YoMoApp %X YoMoApp (YouTube Monitoring App) is an Android app to monitor mobile YouTube video streaming on both application- and network-layer. Additionally, it allows to collect subjective Quality of Experience (QoE) feedback of end users. During the development of the app, the stable versions of YoMoApp were already available in the Google Play Store, and the app was downloaded, installed, and used on many devices to monitor streaming sessions. As the app was not advertised in special campaigns or used for dedicated QoE studies, the monitored streaming sessions of this period compose the data set of a large unsupervised field study. The collected data set is evaluated to characterize current mobile YouTube streaming on both application and network layers. Furthermore, the problems and methodology to obtain QoE results from such unsupervised field study are discussed together with the actual QoE results. Correlations between QoE factors are investigated, and the QoE of clusters of similar streaming sessions is analyzed.

2016[ to top ]

Modeling the YouTube Stack: from Packets to Quality of Experience. Wamser, Florian; Casas, Pedro; Seufert, Michael; Moldovan, Christian; Tran-Gia, Phuoc; Hoßfeld, Tobias. In Computer Networks, 109(2), pp. 211–224. 2016.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
YouTube is one of the most popular and volume-dominant services in today’s Internet, and has changed the Web for ever. Consequently, network operators are forced to consider it in the design, deployment, and optimization of their networks. Taming YouTube requires a good understanding of the complete YouTube stack, from the network streaming service to the application itself. Understanding the interplays between individual YouTube functionalities and their implications for traffic and user Quality of Experience (QoE) becomes paramount nowadays. In this paper we characterize and model the YouTube stack at different layers, going from the generated network traffic to the QoE perceived by the users watching YouTube videos. Firstly, we present a network traffic model for the YouTube flow control mechanism, which permits to understand how YouTube provisions video traffic flows to users. Secondly, we investigate how traffic is consumed at the client side, deriving a simple model for the YouTube application. Thirdly, we analyze the implications for the end user, and present a model for the quality as perceived by them. This model is finally integrated into a system for real time QoE-based YouTube monitoring, highly useful to operators to assess the performance of their networks for provisioning YouTube videos. The central parameter for all the presented models is the buffer level at the YouTube application layer. This paper provides an extensive compendium of objective tools and models for network operators to better understand the YouTube traffic in their networks, to predict the playback behavior of the video player, and to assess how well they do it in the practice with the satisfaction of their customers watching YouTube.

@article{info3-article-2016-6, abstract = {YouTube is one of the most popular and volume-dominant services in today’s Internet, and has changed the Web for ever. Consequently, network operators are forced to consider it in the design, deployment, and optimization of their networks. Taming YouTube requires a good understanding of the complete YouTube stack, from the network streaming service to the application itself. Understanding the interplays between individual YouTube functionalities and their implications for traffic and user Quality of Experience (QoE) becomes paramount nowadays. In this paper we characterize and model the YouTube stack at different layers, going from the generated network traffic to the QoE perceived by the users watching YouTube videos. Firstly, we present a network traffic model for the YouTube flow control mechanism, which permits to understand how YouTube provisions video traffic flows to users. Secondly, we investigate how traffic is consumed at the client side, deriving a simple model for the YouTube application. Thirdly, we analyze the implications for the end user, and present a model for the quality as perceived by them. This model is finally integrated into a system for real time QoE-based YouTube monitoring, highly useful to operators to assess the performance of their networks for provisioning YouTube videos. The central parameter for all the presented models is the buffer level at the YouTube application layer. This paper provides an extensive compendium of objective tools and models for network operators to better understand the YouTube traffic in their networks, to predict the playback behavior of the video player, and to assess how well they do it in the practice with the satisfaction of their customers watching YouTube.}, author = {Wamser, Florian and Casas, Pedro and Seufert, Michael and Moldovan, Christian and Tran-Gia, Phuoc and Hoßfeld, Tobias}, journal = {Computer Networks}, keywords = {can}, month = 11, number = 2, pages = {211-224}, title = {Modeling the YouTube Stack: from Packets to Quality of Experience}, volume = 109, year = 2016 }

%0 Journal Article %1 info3-article-2016-6 %A Wamser, Florian %A Casas, Pedro %A Seufert, Michael %A Moldovan, Christian %A Tran-Gia, Phuoc %A Hoßfeld, Tobias %D 2016 %J Computer Networks %N 2 %P 211-224 %T Modeling the YouTube Stack: from Packets to Quality of Experience %V 109 %X YouTube is one of the most popular and volume-dominant services in today’s Internet, and has changed the Web for ever. Consequently, network operators are forced to consider it in the design, deployment, and optimization of their networks. Taming YouTube requires a good understanding of the complete YouTube stack, from the network streaming service to the application itself. Understanding the interplays between individual YouTube functionalities and their implications for traffic and user Quality of Experience (QoE) becomes paramount nowadays. In this paper we characterize and model the YouTube stack at different layers, going from the generated network traffic to the QoE perceived by the users watching YouTube videos. Firstly, we present a network traffic model for the YouTube flow control mechanism, which permits to understand how YouTube provisions video traffic flows to users. Secondly, we investigate how traffic is consumed at the client side, deriving a simple model for the YouTube application. Thirdly, we analyze the implications for the end user, and present a model for the quality as perceived by them. This model is finally integrated into a system for real time QoE-based YouTube monitoring, highly useful to operators to assess the performance of their networks for provisioning YouTube videos. The central parameter for all the presented models is the buffer level at the YouTube application layer. This paper provides an extensive compendium of objective tools and models for network operators to better understand the YouTube traffic in their networks, to predict the playback behavior of the video player, and to assess how well they do it in the practice with the satisfaction of their customers watching YouTube.

Research Area

Research Area: YouTube Video Streaming

Best Paper Award auf der CNSM 2018

Best Demonstration Award auf der TMA 2018

Demo @ IEEE/IFIP NOMS 2018: QoE Monitoring of the Native Android YouTube App

Selected Findings and Results

Publications

Hinweis zum Datenschutz

Hinweis zum Datenschutz

Bildnachweise