Dr. rer. nat. Christian Moldovan

• An Introduction to Online Video Game QoS and QoE Influencing Factors Metzger, Florian; Geißler, Stefan; Grigorjew, Alexej; Loh, Frank; Moldovan, Christian; Seufert, Michael; Hoßfeld, Tobias in IEEE Communications Surveys & Tutorials (2022).
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  Online video games and cloud gaming are rapidly growing in pervasiveness. Their resource demands can put significant stress on the global communication infrastructure. And network conditions are amongst the chief factors that influence one's enjoyment while playing games. This makes it imperative for video games to be considered for network dimensioning, server placement or protocol development. For that reason, in this work we provide an introduction to the technical aspects of video games in general and of their network aspects in particular. This understanding forms the basis for a rich taxonomy of factors that influence and provide context to a video game's Quality of Service (QoS) and Quality of Experience (QoE). The taxonomy covers influence factors from all aspects involved in a video game, from the subjective player and game influence factors to the system and networking influence factors. Finally, this work gives an overview of conducted and ongoing research as well as future research opportunities while taking into account lessons learned from past approaches.

  @article{info3-article-2022-16, abstract = {Online video games and cloud gaming are rapidly growing in pervasiveness. Their resource demands can put significant stress on the global communication infrastructure. And network conditions are amongst the chief factors that influence one's enjoyment while playing games. This makes it imperative for video games to be considered for network dimensioning, server placement or protocol development. For that reason, in this work we provide an introduction to the technical aspects of video games in general and of their network aspects in particular. This understanding forms the basis for a rich taxonomy of factors that influence and provide context to a video game's Quality of Service (QoS) and Quality of Experience (QoE). The taxonomy covers influence factors from all aspects involved in a video game, from the subjective player and game influence factors to the system and networking influence factors. Finally, this work gives an overview of conducted and ongoing research as well as future research opportunities while taking into account lessons learned from past approaches.}, author = {Metzger, Florian and Geißler, Stefan and Grigorjew, Alexej and Loh, Frank and Moldovan, Christian and Seufert, Michael and Hoßfeld, Tobias}, journal = {IEEE Communications Surveys & Tutorials}, keywords = {sys:hidden:article}, month = 5, title = {An Introduction to Online Video Game QoS and QoE Influencing Factors}, year = 2022 }

  %0 Journal Article %1 info3-article-2022-16 %A Metzger, Florian %A Geißler, Stefan %A Grigorjew, Alexej %A Loh, Frank %A Moldovan, Christian %A Seufert, Michael %A Hoßfeld, Tobias %D 2022 %J IEEE Communications Surveys & Tutorials %T An Introduction to Online Video Game QoS and QoE Influencing Factors %X Online video games and cloud gaming are rapidly growing in pervasiveness. Their resource demands can put significant stress on the global communication infrastructure. And network conditions are amongst the chief factors that influence one's enjoyment while playing games. This makes it imperative for video games to be considered for network dimensioning, server placement or protocol development. For that reason, in this work we provide an introduction to the technical aspects of video games in general and of their network aspects in particular. This understanding forms the basis for a rich taxonomy of factors that influence and provide context to a video game's Quality of Service (QoS) and Quality of Experience (QoE). The taxonomy covers influence factors from all aspects involved in a video game, from the subjective player and game influence factors to the system and networking influence factors. Finally, this work gives an overview of conducted and ongoing research as well as future research opportunities while taking into account lessons learned from past approaches.

• Don’t Stop the Music: Crowdsourced QoE Assessment of Music Streaming with Stalling Schwind, Anika; Moldovan, Christian; Janiak, Tim; Dworschak, Nemo-Dario; Hoßfeld, Tobias in 12th International Conference on Quality of Multimedia Experience (QoMEX) (2020).
  • [ Abstract ]
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  Streaming made a lasting effect on the way our society consumes media in the last decade. While due to streaming the way we listen to music and podcasts has changed drastically, there are very few studies about its Quality of Experience (QoE) and possible influence factors. From video QoE studies, we know that, for example, undesirable stops of the stream (stalling events) have a significant impact on QoE. However, the way in which music and video streaming is consumed differs significantly, as music is often played in the background, and thus, the influence of stalling could be significantly different. Thus, this work evaluates the impact of stalling on music streaming QoE. Therefore, we conduct two crowdsourced user studies: In the first study, users have to rate four songs with different stalling patterns and evaluate the degree of impairments. Afterwards, we compare the ratings to the results of a lab study and show that they are highly correlated, and that crowdsourcing is a suitable way of measuring music streaming QoE. In addition, we conduct a second crowdsourcing study to investigate the influence of the user's attentiveness on QoE. Here, participants have to listen to one song with two stalling events, while one half of them had to transcribe a handwritten text with music playing in the background. The attentiveness shows no influence on the perceived streaming quality, but it shows a significant influence on the perceived quality degradation due to stalling events. Furthermore, considerably more stalling events were missed for workers who focused on the transcription. These results are an important step towards establishing new methods for investigating QoE in multimedia.

  @inproceedings{info3-inproceedings-2020-7, abstract = {Streaming made a lasting effect on the way our society consumes media in the last decade. While due to streaming the way we listen to music and podcasts has changed drastically, there are very few studies about its Quality of Experience (QoE) and possible influence factors. From video QoE studies, we know that, for example, undesirable stops of the stream (stalling events) have a significant impact on QoE. However, the way in which music and video streaming is consumed differs significantly, as music is often played in the background, and thus, the influence of stalling could be significantly different. Thus, this work evaluates the impact of stalling on music streaming QoE. Therefore, we conduct two crowdsourced user studies: In the first study, users have to rate four songs with different stalling patterns and evaluate the degree of impairments. Afterwards, we compare the ratings to the results of a lab study and show that they are highly correlated, and that crowdsourcing is a suitable way of measuring music streaming QoE. In addition, we conduct a second crowdsourcing study to investigate the influence of the user's attentiveness on QoE. Here, participants have to listen to one song with two stalling events, while one half of them had to transcribe a handwritten text with music playing in the background. The attentiveness shows no influence on the perceived streaming quality, but it shows a significant influence on the perceived quality degradation due to stalling events. Furthermore, considerably more stalling events were missed for workers who focused on the transcription. These results are an important step towards establishing new methods for investigating QoE in multimedia.}, address = {Athlone, Ireland}, author = {Schwind, Anika and Moldovan, Christian and Janiak, Tim and Dworschak, Nemo-Dario and Hoßfeld, Tobias}, booktitle = {12th International Conference on Quality of Multimedia Experience (QoMEX)}, keywords = {ua-net}, month = 5, title = {Don’t Stop the Music: Crowdsourced QoE Assessment of Music Streaming with Stalling}, year = 2020 }

  %0 Conference Paper %1 info3-inproceedings-2020-7 %A Schwind, Anika %A Moldovan, Christian %A Janiak, Tim %A Dworschak, Nemo-Dario %A Hoßfeld, Tobias %B 12th International Conference on Quality of Multimedia Experience (QoMEX) %C Athlone, Ireland %D 2020 %T Don’t Stop the Music: Crowdsourced QoE Assessment of Music Streaming with Stalling %X Streaming made a lasting effect on the way our society consumes media in the last decade. While due to streaming the way we listen to music and podcasts has changed drastically, there are very few studies about its Quality of Experience (QoE) and possible influence factors. From video QoE studies, we know that, for example, undesirable stops of the stream (stalling events) have a significant impact on QoE. However, the way in which music and video streaming is consumed differs significantly, as music is often played in the background, and thus, the influence of stalling could be significantly different. Thus, this work evaluates the impact of stalling on music streaming QoE. Therefore, we conduct two crowdsourced user studies: In the first study, users have to rate four songs with different stalling patterns and evaluate the degree of impairments. Afterwards, we compare the ratings to the results of a lab study and show that they are highly correlated, and that crowdsourcing is a suitable way of measuring music streaming QoE. In addition, we conduct a second crowdsourcing study to investigate the influence of the user's attentiveness on QoE. Here, participants have to listen to one song with two stalling events, while one half of them had to transcribe a handwritten text with music playing in the background. The attentiveness shows no influence on the perceived streaming quality, but it shows a significant influence on the perceived quality degradation due to stalling events. Furthermore, considerably more stalling events were missed for workers who focused on the transcription. These results are an important step towards establishing new methods for investigating QoE in multimedia.
• Is the Uplink Enough? Estimating Video Stalls from Encrypted Network Traffic Loh, Frank; Wamser, Florian; Moldovan, Christian; Zeidler, Bernd; Tsilimantos, Dimitrios; Valentin, Stefan; Hoßfeld, Tobias in 32th IEEE/IFIP Network Operations and Management Symposium (2020).
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  Today’s traffic projections speak of almost 58 % video traffic across the Internet. Nearly all video traffic is encrypted, accounting for more than 50 % encrypted traffic worldwide. To analyze video traffic today, or even estimate its quality in the network, a deep look into the traffic characteristics has to be done. But then, important quality metrics from the traffic behavior can be derived. Based on extensive measurements we show in this work how to measure and estimate video stalls for mobile adaptive streaming. The underlying dataset includes more than 900 hours of video footage from the native YouTube app, measured over 18 different videos in 56 network scenarios in two cities in Europe. We outline a possible approach to estimate the video playback buffer size based on uplink video chunk requests in real-time to break down the video stalls. This work is intended as a tool for network operators to receive further knowledge of the characteristics of video streaming traffic to quantify the most important QoE degradation factors of one of the most important applications today.

  @inproceedings{info3-inproceedings-2020-2, abstract = {Today’s traffic projections speak of almost 58 % video traffic across the Internet. Nearly all video traffic is encrypted, accounting for more than 50 % encrypted traffic worldwide. To analyze video traffic today, or even estimate its quality in the network, a deep look into the traffic characteristics has to be done. But then, important quality metrics from the traffic behavior can be derived. Based on extensive measurements we show in this work how to measure and estimate video stalls for mobile adaptive streaming. The underlying dataset includes more than 900 hours of video footage from the native YouTube app, measured over 18 different videos in 56 network scenarios in two cities in Europe. We outline a possible approach to estimate the video playback buffer size based on uplink video chunk requests in real-time to break down the video stalls. This work is intended as a tool for network operators to receive further knowledge of the characteristics of video streaming traffic to quantify the most important QoE degradation factors of one of the most important applications today.}, address = {Budapest, Hungary}, author = {Loh, Frank and Wamser, Florian and Moldovan, Christian and Zeidler, Bernd and Tsilimantos, Dimitrios and Valentin, Stefan and Hoßfeld, Tobias}, booktitle = {32th IEEE/IFIP Network Operations and Management Symposium}, keywords = {can}, month = 4, title = {Is the Uplink Enough? Estimating Video Stalls from Encrypted Network Traffic}, year = 2020 }

  %0 Conference Paper %1 info3-inproceedings-2020-2 %A Loh, Frank %A Wamser, Florian %A Moldovan, Christian %A Zeidler, Bernd %A Tsilimantos, Dimitrios %A Valentin, Stefan %A Hoßfeld, Tobias %B 32th IEEE/IFIP Network Operations and Management Symposium %C Budapest, Hungary %D 2020 %T Is the Uplink Enough? Estimating Video Stalls from Encrypted Network Traffic %X Today’s traffic projections speak of almost 58 % video traffic across the Internet. Nearly all video traffic is encrypted, accounting for more than 50 % encrypted traffic worldwide. To analyze video traffic today, or even estimate its quality in the network, a deep look into the traffic characteristics has to be done. But then, important quality metrics from the traffic behavior can be derived. Based on extensive measurements we show in this work how to measure and estimate video stalls for mobile adaptive streaming. The underlying dataset includes more than 900 hours of video footage from the native YouTube app, measured over 18 different videos in 56 network scenarios in two cities in Europe. We outline a possible approach to estimate the video playback buffer size based on uplink video chunk requests in real-time to break down the video stalls. This work is intended as a tool for network operators to receive further knowledge of the characteristics of video streaming traffic to quantify the most important QoE degradation factors of one of the most important applications today.
• Optimizing HAS for 360-Degree Videos Moldovan, Christian; Loh, Frank; Seufert, Michael; Hoßfeld, Tobias in 5th IEEE/IFIP International Workshop on Analytics for Network and Service Management (AnNet) (2020).
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  @inproceedings{info3-inproceedings-2020-1, address = {Budapest, Hungary}, author = {Moldovan, Christian and Loh, Frank and Seufert, Michael and Hoßfeld, Tobias}, booktitle = {5th IEEE/IFIP International Workshop on Analytics for Network and Service Management (AnNet)}, keywords = {net-man}, month = 4, title = {Optimizing HAS for 360-Degree Videos}, year = 2020 }

  %0 Conference Paper %1 info3-inproceedings-2020-1 %A Moldovan, Christian %A Loh, Frank %A Seufert, Michael %A Hoßfeld, Tobias %B 5th IEEE/IFIP International Workshop on Analytics for Network and Service Management (AnNet) %C Budapest, Hungary %D 2020 %T Optimizing HAS for 360-Degree Videos

• Energy-Efficient Adaptation Logic for HTTP Streaming in Mobile Networks Moldovan, Christian; Wamser, Florian; Hoßfeld, Tobias in 2019 International Conference on Networked Systems (NetSys) (2019).
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  The requirements for video streaming have changed drastically during the past years. In today's Internet, high definition resolutions are considered default for videos, even in mobile settings, and with 4G penetration reaching 90 percent in the US, this no longer poses a big problem. However, while mobile bandwidth has increased, the battery life time of mobile devices has not increased significantly. Furthermore, current data plans are still not large enough to regularly stream movies during the commute. Users still resort to downloading media before travel. In this paper we propose a new HTTP adaptive streaming algorithm that delivers videos in high quality while avoiding stalling events, schedules the download of video segments so that a energy conserving idle state is often reached and keeps the buffer low at points in the video where many viewers abandon the video to save data. While most adaptive streaming algorithms optimize quality and stalling, this is the first attempt to use an adaptive streaming algorithm to reduce energy consumption. Since video streaming providers mostly care about the Quality of Experience when watching videos, energy efficiency is left to the device manufacturers. Therefore, both parties have little incentive to cooperate in this regard. But on the Internet of tomorrow, where most videos are watched on mobile devices, energy efficiency and the Quality of Experience must go hand in hand.

  @inproceedings{info3-inproceedings-2019-9, abstract = {The requirements for video streaming have changed drastically during the past years. In today's Internet, high definition resolutions are considered default for videos, even in mobile settings, and with 4G penetration reaching 90 percent in the US, this no longer poses a big problem. However, while mobile bandwidth has increased, the battery life time of mobile devices has not increased significantly. Furthermore, current data plans are still not large enough to regularly stream movies during the commute. Users still resort to downloading media before travel. In this paper we propose a new HTTP adaptive streaming algorithm that delivers videos in high quality while avoiding stalling events, schedules the download of video segments so that a energy conserving idle state is often reached and keeps the buffer low at points in the video where many viewers abandon the video to save data. While most adaptive streaming algorithms optimize quality and stalling, this is the first attempt to use an adaptive streaming algorithm to reduce energy consumption. Since video streaming providers mostly care about the Quality of Experience when watching videos, energy efficiency is left to the device manufacturers. Therefore, both parties have little incentive to cooperate in this regard. But on the Internet of tomorrow, where most videos are watched on mobile devices, energy efficiency and the Quality of Experience must go hand in hand.}, author = {Moldovan, Christian and Wamser, Florian and Hoßfeld, Tobias}, booktitle = {2019 International Conference on Networked Systems (NetSys)}, keywords = {energy-efficiency}, month = 3, title = {Energy-Efficient Adaptation Logic for HTTP Streaming in Mobile Networks}, year = 2019 }

  %0 Conference Paper %1 info3-inproceedings-2019-9 %A Moldovan, Christian %A Wamser, Florian %A Hoßfeld, Tobias %B 2019 International Conference on Networked Systems (NetSys) %D 2019 %T Energy-Efficient Adaptation Logic for HTTP Streaming in Mobile Networks %X The requirements for video streaming have changed drastically during the past years. In today's Internet, high definition resolutions are considered default for videos, even in mobile settings, and with 4G penetration reaching 90 percent in the US, this no longer poses a big problem. However, while mobile bandwidth has increased, the battery life time of mobile devices has not increased significantly. Furthermore, current data plans are still not large enough to regularly stream movies during the commute. Users still resort to downloading media before travel. In this paper we propose a new HTTP adaptive streaming algorithm that delivers videos in high quality while avoiding stalling events, schedules the download of video segments so that a energy conserving idle state is often reached and keeps the buffer low at points in the video where many viewers abandon the video to save data. While most adaptive streaming algorithms optimize quality and stalling, this is the first attempt to use an adaptive streaming algorithm to reduce energy consumption. Since video streaming providers mostly care about the Quality of Experience when watching videos, energy efficiency is left to the device manufacturers. Therefore, both parties have little incentive to cooperate in this regard. But on the Internet of tomorrow, where most videos are watched on mobile devices, energy efficiency and the Quality of Experience must go hand in hand.
• User Behavior and Engagement of a Mobile Video Streaming User from Crowdsourced Measurements Moldovan, Christian; Wamser, Florian; Hoßfeld, Tobias in 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX) (2019).
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  Mobile video streaming has gained a lot of popularity in recent year with the introduction of large data plans for mobile phones. While users in non-mobile scenarios have become accustomed to high quality and few stalling events, this is not the case in mobile environments. People are more likely to tolerate stalling since they know high throughput coverage cannot always be guaranteed. Their behavior and their engagement are drastically different when watching videos on mobile devices. In this paper, we characterize mobile phone users who use a video streaming application. We present a data set of over 6,000 video views from a crowdsourced video measurement study. We investigate user activity and engagement during video streaming and how such metrics are correlated with each other. This is the first study that goes beyond user engagement and investigates the direct behavior of mobile video users outside the lab which is an important step towards mobile QoE management.

  @inproceedings{info3-inproceedings-2019-21, abstract = {Mobile video streaming has gained a lot of popularity in recent year with the introduction of large data plans for mobile phones. While users in non-mobile scenarios have become accustomed to high quality and few stalling events, this is not the case in mobile environments. People are more likely to tolerate stalling since they know high throughput coverage cannot always be guaranteed. Their behavior and their engagement are drastically different when watching videos on mobile devices. In this paper, we characterize mobile phone users who use a video streaming application. We present a data set of over 6,000 video views from a crowdsourced video measurement study. We investigate user activity and engagement during video streaming and how such metrics are correlated with each other. This is the first study that goes beyond user engagement and investigates the direct behavior of mobile video users outside the lab which is an important step towards mobile QoE management.}, author = {Moldovan, Christian and Wamser, Florian and Hoßfeld, Tobias}, booktitle = {2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX)}, keywords = {can}, month = 6, title = {User Behavior and Engagement of a Mobile Video Streaming User from Crowdsourced Measurements}, year = 2019 }

  %0 Conference Paper %1 info3-inproceedings-2019-21 %A Moldovan, Christian %A Wamser, Florian %A Hoßfeld, Tobias %B 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX) %D 2019 %T User Behavior and Engagement of a Mobile Video Streaming User from Crowdsourced Measurements %X Mobile video streaming has gained a lot of popularity in recent year with the introduction of large data plans for mobile phones. While users in non-mobile scenarios have become accustomed to high quality and few stalling events, this is not the case in mobile environments. People are more likely to tolerate stalling since they know high throughput coverage cannot always be guaranteed. Their behavior and their engagement are drastically different when watching videos on mobile devices. In this paper, we characterize mobile phone users who use a video streaming application. We present a data set of over 6,000 video views from a crowdsourced video measurement study. We investigate user activity and engagement during video streaming and how such metrics are correlated with each other. This is the first study that goes beyond user engagement and investigates the direct behavior of mobile video users outside the lab which is an important step towards mobile QoE management.
• User Behavior and Engagement of a Mobile Video Streaming User from Crowdsourced Measurements Moldovan, Christian (2019, June).
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  @misc{info3-poster-2019-1, address = {Berlin, Germany}, author = {Moldovan, Christian}, booktitle = {2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX)}, keywords = {sys:hidden:poster}, month = 6, title = {User Behavior and Engagement of a Mobile Video Streaming User from Crowdsourced Measurements}, year = 2019 }

  %0 Generic %1 info3-poster-2019-1 %A Moldovan, Christian %B 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX) %C Berlin, Germany %D 2019 %T User Behavior and Engagement of a Mobile Video Streaming User from Crowdsourced Measurements
• Peeking under the Hood – How the Measurement Setup Influences the Video Streaming Behavior Schwind, Anika; Janiak, Lea; Moldovan, Christian; Wamser, Florian; Hoßfeld, Tobias in The 3rd International Workshop on Quality of Experience Management (QoE-Management 2019) (2019).
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  Global Internet video traffic will dramatically increase in the next years. With this rapid growth, interest in the application behavior of video streaming services and the resulting user experience rises. In particular, there is a need to understand the influence of system parameters on the streaming performance. Thus, several monitoring approaches have been developed which allow conducting automated measurements on a large-scale, for example, lightweight approaches for measurements running in the mobile networks or setups using a virtual frame buffer for servers without a display running in virtualized cloud environments. In some cases, the measurement hardware can totally be controlled, in other cases, there is no knowledge about parallel running services. In this paper, we answer the question whether and how much the measurement setup (e.g., virtualization, headless browsers, load on machines) influences the video streaming behavior. Therefore, we compare eight management setups with the ground truth (an end user watching a video on the own device) by evaluating the key performance indicators on application layer during video streaming, i.e. initial video playout delays and stalling. Our results reveal important insights: some of the common measurement setups heavily influence the measurements and must be avoided to collect reliable results.

  @inproceedings{info3-inproceedings-2019-6, abstract = {Global Internet video traffic will dramatically increase in the next years. With this rapid growth, interest in the application behavior of video streaming services and the resulting user experience rises. In particular, there is a need to understand the influence of system parameters on the streaming performance. Thus, several monitoring approaches have been developed which allow conducting automated measurements on a large-scale, for example, lightweight approaches for measurements running in the mobile networks or setups using a virtual frame buffer for servers without a display running in virtualized cloud environments. In some cases, the measurement hardware can totally be controlled, in other cases, there is no knowledge about parallel running services. In this paper, we answer the question whether and how much the measurement setup (e.g., virtualization, headless browsers, load on machines) influences the video streaming behavior. Therefore, we compare eight management setups with the ground truth (an end user watching a video on the own device) by evaluating the key performance indicators on application layer during video streaming, i.e. initial video playout delays and stalling. Our results reveal important insights: some of the common measurement setups heavily influence the measurements and must be avoided to collect reliable results.}, address = {Paris, France}, author = {Schwind, Anika and Janiak, Lea and Moldovan, Christian and Wamser, Florian and Hoßfeld, Tobias}, booktitle = {The 3rd International Workshop on Quality of Experience Management (QoE-Management 2019)}, keywords = {ua-net}, month = 2, title = {Peeking under the Hood – How the Measurement Setup Influences the Video Streaming Behavior}, year = 2019 }

  %0 Conference Paper %1 info3-inproceedings-2019-6 %A Schwind, Anika %A Janiak, Lea %A Moldovan, Christian %A Wamser, Florian %A Hoßfeld, Tobias %B The 3rd International Workshop on Quality of Experience Management (QoE-Management 2019) %C Paris, France %D 2019 %T Peeking under the Hood – How the Measurement Setup Influences the Video Streaming Behavior %X Global Internet video traffic will dramatically increase in the next years. With this rapid growth, interest in the application behavior of video streaming services and the resulting user experience rises. In particular, there is a need to understand the influence of system parameters on the streaming performance. Thus, several monitoring approaches have been developed which allow conducting automated measurements on a large-scale, for example, lightweight approaches for measurements running in the mobile networks or setups using a virtual frame buffer for servers without a display running in virtualized cloud environments. In some cases, the measurement hardware can totally be controlled, in other cases, there is no knowledge about parallel running services. In this paper, we answer the question whether and how much the measurement setup (e.g., virtualization, headless browsers, load on machines) influences the video streaming behavior. Therefore, we compare eight management setups with the ground truth (an end user watching a video on the own device) by evaluating the key performance indicators on application layer during video streaming, i.e. initial video playout delays and stalling. Our results reveal important insights: some of the common measurement setups heavily influence the measurements and must be avoided to collect reliable results.
• Energy-Efficient Adaptation Logic for HTTP Streaming in Mobile Networks Moldovan, Christian (2019, March 20).
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  @presentation{info3-presentation-2019-7, address = {2019 International Conference on Networked Systems (NetSys), Garching, Germany}, author = {Moldovan, Christian}, keywords = {sys:hidden:presentation}, month = 3, title = {Energy-Efficient Adaptation Logic for HTTP Streaming in Mobile Networks}, year = 2019 }

  %0 Generic %1 info3-presentation-2019-7 %A Moldovan, Christian %C 2019 International Conference on Networked Systems (NetSys), Garching, Germany %D 2019 %T Energy-Efficient Adaptation Logic for HTTP Streaming in Mobile Networks
• Contributions to TF "Managing Web & Cloud QoE" Moldovan, Christian (2019, June 4).
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  @presentation{info3-presentation-2019-6, address = {13th Qualinet General Meeting, Berlin, Germany}, author = {Moldovan, Christian}, keywords = {sys:hidden:presentation}, month = 6, title = {Contributions to TF "Managing Web & Cloud QoE"}, year = 2019 }

  %0 Generic %1 info3-presentation-2019-6 %A Moldovan, Christian %C 13th Qualinet General Meeting, Berlin, Germany %D 2019 %T Contributions to TF "Managing Web & Cloud QoE"
• From click to playback: a dataset to study the response time of mobile YouTube Loh, Frank; Wamser, Florian; Moldovan, Christian; Zeidler, Bernd; Hoßfeld, Tobias; Tsilimantos, Dimitrios; Valentin, Stefan in Proceedings of the 10th ACM Multimedia Systems Conference (2019). 267–272.
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  Responding fluently to user requests is important to keep them immersed. In this paper, we are presenting an extensive dataset to study the response time of YouTube's mobile video streaming service on Android. We illustrate the application of our dataset by studying YouTube's initial delay for a subset of 9 videos in 75 network scenarios. We find that in 41% of the cases, YouTube exceeds the attention span of a typical user, while deep immersion is only reached in 15% of the cases. Our factor analysis implies that the allocation of the initial CDN node is the critical link in this delay chain. Since our dataset includes a large variety of factors, we are describing setup, methodology, and data structure in detail. Our dataset and measurement tools are publicly available.

  @inproceedings{info3-inproceedings-2019-25, abstract = {Responding fluently to user requests is important to keep them immersed. In this paper, we are presenting an extensive dataset to study the response time of YouTube's mobile video streaming service on Android. We illustrate the application of our dataset by studying YouTube's initial delay for a subset of 9 videos in 75 network scenarios. We find that in 41% of the cases, YouTube exceeds the attention span of a typical user, while deep immersion is only reached in 15% of the cases. Our factor analysis implies that the allocation of the initial CDN node is the critical link in this delay chain. Since our dataset includes a large variety of factors, we are describing setup, methodology, and data structure in detail. Our dataset and measurement tools are publicly available.}, author = {Loh, Frank and Wamser, Florian and Moldovan, Christian and Zeidler, Bernd and Hoßfeld, Tobias and Tsilimantos, Dimitrios and Valentin, Stefan}, booktitle = {Proceedings of the 10th ACM Multimedia Systems Conference}, keywords = {can}, month = 6, pages = {267-272}, title = {From click to playback: a dataset to study the response time of mobile YouTube}, year = 2019 }

  %0 Conference Paper %1 info3-inproceedings-2019-25 %A Loh, Frank %A Wamser, Florian %A Moldovan, Christian %A Zeidler, Bernd %A Hoßfeld, Tobias %A Tsilimantos, Dimitrios %A Valentin, Stefan %B Proceedings of the 10th ACM Multimedia Systems Conference %D 2019 %P 267-272 %T From click to playback: a dataset to study the response time of mobile YouTube %X Responding fluently to user requests is important to keep them immersed. In this paper, we are presenting an extensive dataset to study the response time of YouTube's mobile video streaming service on Android. We illustrate the application of our dataset by studying YouTube's initial delay for a subset of 9 videos in 75 network scenarios. We find that in 41% of the cases, YouTube exceeds the attention span of a typical user, while deep immersion is only reached in 15% of the cases. Our factor analysis implies that the allocation of the initial CDN node is the critical link in this delay chain. Since our dataset includes a large variety of factors, we are describing setup, methodology, and data structure in detail. Our dataset and measurement tools are publicly available.

• Optimal Fairness and Quality in Video Streaming With Multiple Users Moldovan, Christian (2018, September 4).
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  @presentation{info3-presentation-2018-6, address = {30th International Teletraffic Congress (ITC 30), Vienna, Austria}, author = {Moldovan, Christian}, keywords = {sys:hidden:presentation}, month = 9, title = {Optimal Fairness and Quality in Video Streaming With Multiple Users}, year = 2018 }

  %0 Generic %1 info3-presentation-2018-6 %A Moldovan, Christian %C 30th International Teletraffic Congress (ITC 30), Vienna, Austria %D 2018 %T Optimal Fairness and Quality in Video Streaming With Multiple Users
• Towards Machine Learning-Based Optimal HAS Sieber, Christian; Hagn, Korbinian; Moldovan, Christian; Hoßfeld, Tobias; Kellerer, Wolfgang in arXiv preprint arXiv:1808.08065 (2018).
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  @article{info3-article-2018-8, author = {Sieber, Christian and Hagn, Korbinian and Moldovan, Christian and Hoßfeld, Tobias and Kellerer, Wolfgang}, journal = {arXiv preprint arXiv:1808.08065}, keywords = {sys:hidden:article}, title = {Towards Machine Learning-Based Optimal HAS}, year = 2018 }

  %0 Journal Article %1 info3-article-2018-8 %A Sieber, Christian %A Hagn, Korbinian %A Moldovan, Christian %A Hoßfeld, Tobias %A Kellerer, Wolfgang %D 2018 %J arXiv preprint arXiv:1808.08065 %T Towards Machine Learning-Based Optimal HAS

• Keep Calm and Don’t Switch: About the Relationship Between Switches and Quality in HAS Moldovan, Christian (2017, September 8).
  • [ BibTeX ]
  • [ EndNote ]
  @misc{info3-presentation-2017-13, address = {Genoa, Italy}, author = {Moldovan, Christian}, booktitle = {29th International Teletraffic Congress (ITC 29)}, keywords = {sys:hidden:poster}, month = 9, title = {Keep Calm and Don’t Switch: About the Relationship Between Switches and Quality in HAS}, year = 2017 }

  %0 Generic %1 info3-presentation-2017-13 %A Moldovan, Christian %B 29th International Teletraffic Congress (ITC 29) %C Genoa, Italy %D 2017 %T Keep Calm and Don’t Switch: About the Relationship Between Switches and Quality in HAS
• Viability of Wi-Fi Caches in an Era of HTTPS Prevalence Moldovan, Christian (2017, May 25).
  • [ BibTeX ]
  • [ EndNote ]
  @presentation{info3-presentation-2017-12, address = {2017 IEEE International Conference on Communications Workshops (ICC Workshops), Paris, France}, author = {Moldovan, Christian}, keywords = {sys:hidden:presentation}, month = 5, title = {Viability of Wi-Fi Caches in an Era of HTTPS Prevalence}, year = 2017 }

  %0 Generic %1 info3-presentation-2017-12 %A Moldovan, Christian %C 2017 IEEE International Conference on Communications Workshops (ICC Workshops), Paris, France %D 2017 %T Viability of Wi-Fi Caches in an Era of HTTPS Prevalence

• Bridging the Gap between QoE and User Engagement in HTTP Video Streaming Moldovan, Christian (2016, September 14).
  • [ BibTeX ]
  • [ EndNote ]
  @presentation{info3-presentation-2016-13, address = {28th International Teletraffic Congress (ITC 28), Würzburg, Germany}, author = {Moldovan, Christian}, keywords = {sys:hidden:presentation}, month = 9, title = {Bridging the Gap between QoE and User Engagement in HTTP Video Streaming}, year = 2016 }

  %0 Generic %1 info3-presentation-2016-13 %A Moldovan, Christian %C 28th International Teletraffic Congress (ITC 28), Würzburg, Germany %D 2016 %T Bridging the Gap between QoE and User Engagement in HTTP Video Streaming
• Impact of Variances on the QoE in Video Streaming Moldovan, Christian (2016, September 16).
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  • [ EndNote ]
  @presentation{info3-presentation-2016-12, address = {ITC and IEEE Workshop on QoE Centric Management (QCMan), Würzburg, Germany}, author = {Moldovan, Christian}, keywords = {sys:hidden:presentation}, month = 9, title = {Impact of Variances on the QoE in Video Streaming}, year = 2016 }

  %0 Generic %1 info3-presentation-2016-12 %A Moldovan, Christian %C ITC and IEEE Workshop on QoE Centric Management (QCMan), Würzburg, Germany %D 2016 %T Impact of Variances on the QoE in Video Streaming
• QoE in Video Streaming - Example: Context Awareness Moldovan, Christian (2016, August 31).
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  • [ EndNote ]
  @presentation{info3-presentation-2016-14, address = {ITG 5.2.1 Workshop Performance Evaluation and Optimisation of Communication Networks, Hamburg, Germany}, author = {Moldovan, Christian}, keywords = {sys:hidden:presentation}, month = 8, title = {QoE in Video Streaming - Example: Context Awareness}, year = 2016 }

  %0 Generic %1 info3-presentation-2016-14 %A Moldovan, Christian %C ITG 5.2.1 Workshop Performance Evaluation and Optimisation of Communication Networks, Hamburg, Germany %D 2016 %T QoE in Video Streaming - Example: Context Awareness
• YouTube Can Do Better: Getting the Most Out of Video Adaptation Moldovan, Christian (2016, September 16).
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  @presentation{info3-presentation-2016-11, address = {ITC and IEEE Workshop on QoE Centric Management (QCMan), Würzburg, Germany}, author = {Moldovan, Christian}, keywords = {sys:hidden:presentation}, month = 9, title = {YouTube Can Do Better: Getting the Most Out of Video Adaptation}, year = 2016 }

  %0 Generic %1 info3-presentation-2016-11 %A Moldovan, Christian %C ITC and IEEE Workshop on QoE Centric Management (QCMan), Würzburg, Germany %D 2016 %T YouTube Can Do Better: Getting the Most Out of Video Adaptation

• To Each According to his Needs: Dimensioning Video Buffer for Specific User Profiles and Behavior Moldovan, Christian (2015, May 11).
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  @presentation{info3-presentation-2015-1, address = {Third IFIP/IEEE International Workshop on Quality of Experience Centric Management, Ottawa, Canada}, author = {Moldovan, Christian}, keywords = {sys:hidden:presentation}, month = 5, title = {To Each According to his Needs: Dimensioning Video Buffer for Specific User Profiles and Behavior}, year = 2015 }

  %0 Generic %1 info3-presentation-2015-1 %A Moldovan, Christian %C Third IFIP/IEEE International Workshop on Quality of Experience Centric Management, Ottawa, Canada %D 2015 %T To Each According to his Needs: Dimensioning Video Buffer for Specific User Profiles and Behavior