Video Quality Monitoring in Future Networks

Project Team

Head		Dr. Thomas Zinner

Researchers		Steffen Gebert Matthias Hartmann David Hock Michael Jarschel Dominik Klein
Students		Nicholas Gray Florian Mayer Michael Rüger Johann Scherer Xiajun Yan

In the past decade, video streaming has taken over a large part of the current Internet traffic and is predominant on mobile devices. With the growing expectations of video consumers with respect to the service quality, monitoring is an important aspect for network providers to be able to rapidly react on performance problems or high network load and hence prevent service degradations. In parallel, emerging technologies like software defined networking or network virtualization introduce support for specialized networks which even allow enhanced functionality of nodes in the network. This development enables more sophisticated monitoring techniques in the network which use detailed knowledge about the video content to better predict the service quality at consumers.

Monitoring Based on Pre-Computed Frame Distortions

The first presented monitoring approach is a user-centric monitoring technique which uses precomputed knowledge about the video content in order to assess the service quality of video consumers. Therefore, we distribute video monitoring functionality to intermediate nodes in the network. The proposed monitoring technique takes into account the quality degradation due to lost frames and stores that information on the monitoring nodes in the network. The computation is based on the structural similarity (SSIM) metric and uses a mapping from SSIM to video quality in order to predict the quality at the video consumer.

Article[ to top ]

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.
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@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

Inproceedings[ to top ]

Optimal Fairness and Quality in Video Streaming With Multiple Users. Moldovan, Christian; Skorin-Kapov, Lea; Heegaard, Poul E.; Hoßfeld, Tobias. In 30th International Teletraffic Congress (ITC 30), p. 6. Vienna, Austria, 2018.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
With the majority of video distribution services relying on the HTTP adaptive streaming paradigm, a great deal of research is geared towards developing algorithms and solutions for improving user perceived quality while making efficient use of available resources. Our goal is to provide the means for benchmarking such solutions in the context of multiple users accessing Video on Demand content while sharing a bottleneck link. For that purpose, we propose a quadratic problem formulation to compute the theoretical optimum in terms of adaptation strategies and corresponding segment downloads across multiple users under given bandwidth constraints. By aiming to maximize both service quality and fairness, we quantify and compare the impact of different fairness objectives (bandwidth fairness, pattern fairness, and session fairness) on resulting quality and achieved QoE fairness. Based on conducted simulations and parameter studies, our results demonstrate the benefits of optimizing for session fairness as compared to other approaches.

@inproceedings{info3-inproceedings-2018-37, abstract = {With the majority of video distribution services relying on the HTTP adaptive streaming paradigm, a great deal of research is geared towards developing algorithms and solutions for improving user perceived quality while making efficient use of available resources. Our goal is to provide the means for benchmarking such solutions in the context of multiple users accessing Video on Demand content while sharing a bottleneck link. For that purpose, we propose a quadratic problem formulation to compute the theoretical optimum in terms of adaptation strategies and corresponding segment downloads across multiple users under given bandwidth constraints. By aiming to maximize both service quality and fairness, we quantify and compare the impact of different fairness objectives (bandwidth fairness, pattern fairness, and session fairness) on resulting quality and achieved QoE fairness. Based on conducted simulations and parameter studies, our results demonstrate the benefits of optimizing for session fairness as compared to other approaches.}, address = {Vienna, Austria}, author = {Moldovan, Christian and Skorin-Kapov, Lea and Heegaard, Poul E. and Hoßfeld, Tobias}, booktitle = {30th International Teletraffic Congress (ITC 30)}, keywords = {video-quality}, month = {09}, pages = 6, title = {Optimal Fairness and Quality in Video Streaming With Multiple Users}, year = 2018 }

%0 Conference Paper %1 info3-inproceedings-2018-37 %A Moldovan, Christian %A Skorin-Kapov, Lea %A Heegaard, Poul E. %A Hoßfeld, Tobias %B 30th International Teletraffic Congress (ITC 30) %C Vienna, Austria %D 2018 %P 6 %T Optimal Fairness and Quality in Video Streaming With Multiple Users %X With the majority of video distribution services relying on the HTTP adaptive streaming paradigm, a great deal of research is geared towards developing algorithms and solutions for improving user perceived quality while making efficient use of available resources. Our goal is to provide the means for benchmarking such solutions in the context of multiple users accessing Video on Demand content while sharing a bottleneck link. For that purpose, we propose a quadratic problem formulation to compute the theoretical optimum in terms of adaptation strategies and corresponding segment downloads across multiple users under given bandwidth constraints. By aiming to maximize both service quality and fairness, we quantify and compare the impact of different fairness objectives (bandwidth fairness, pattern fairness, and session fairness) on resulting quality and achieved QoE fairness. Based on conducted simulations and parameter studies, our results demonstrate the benefits of optimizing for session fairness as compared to other approaches.
Keep Calm and Don’t Switch: About the Relationship Between Switches and Quality in HAS. Moldovan, Christian; Hagn, Korbinian; Sieber, Christian; Kellerer, Wolfgang; Hoßfeld, Tobias. In 29th International Teletraffic Congress (ITC 29), p. 6. Genoa, Italy, 2017.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
Video streaming has established itself as the main method to consume multimedia content on the Internet. The most widespread technique is HTTP Adaptive Streaming which is used by large video service platforms such as YouTube and Netflix. In order to guarantee a high QoE, different adaptation strategies have been developed that lead to different video playout patterns. While some strategies may be more aggressive than others and often adapt the video quality, it is not clear which strategy is the best. In this paper, we want to identify the trade-off between the av- erage video quality and switches in the quality during playout. We do this with a user-centric view and try to optimize the adaptation depending on the user preference with a quadratic program. This work allows us to put existing and future video adaptation algorithms in perspective with respect to user preferences. Our results show that the video quality can already be increased greatly by allowing few switches while more switches lead to diminishing gains. This is a novel discovery that is important for user-centric QoE-management which is of high interest for ISPs and video service providers.

@inproceedings{info3-inproceedings-2017-26, abstract = {Video streaming has established itself as the main method to consume multimedia content on the Internet. The most widespread technique is HTTP Adaptive Streaming which is used by large video service platforms such as YouTube and Netflix. In order to guarantee a high QoE, different adaptation strategies have been developed that lead to different video playout patterns. While some strategies may be more aggressive than others and often adapt the video quality, it is not clear which strategy is the best. In this paper, we want to identify the trade-off between the av- erage video quality and switches in the quality during playout. We do this with a user-centric view and try to optimize the adaptation depending on the user preference with a quadratic program. This work allows us to put existing and future video adaptation algorithms in perspective with respect to user preferences. Our results show that the video quality can already be increased greatly by allowing few switches while more switches lead to diminishing gains. This is a novel discovery that is important for user-centric QoE-management which is of high interest for ISPs and video service providers.}, address = {Genoa, Italy}, author = {Moldovan, Christian and Hagn, Korbinian and Sieber, Christian and Kellerer, Wolfgang and Hoßfeld, Tobias}, booktitle = {29th International Teletraffic Congress (ITC 29)}, keywords = {video-quality}, month = {09}, pages = 6, title = {Keep Calm and Don’t Switch: About the Relationship Between Switches and Quality in HAS}, year = 2017 }

%0 Conference Paper %1 info3-inproceedings-2017-26 %A Moldovan, Christian %A Hagn, Korbinian %A Sieber, Christian %A Kellerer, Wolfgang %A Hoßfeld, Tobias %B 29th International Teletraffic Congress (ITC 29) %C Genoa, Italy %D 2017 %P 6 %T Keep Calm and Don’t Switch: About the Relationship Between Switches and Quality in HAS %X Video streaming has established itself as the main method to consume multimedia content on the Internet. The most widespread technique is HTTP Adaptive Streaming which is used by large video service platforms such as YouTube and Netflix. In order to guarantee a high QoE, different adaptation strategies have been developed that lead to different video playout patterns. While some strategies may be more aggressive than others and often adapt the video quality, it is not clear which strategy is the best. In this paper, we want to identify the trade-off between the av- erage video quality and switches in the quality during playout. We do this with a user-centric view and try to optimize the adaptation depending on the user preference with a quadratic program. This work allows us to put existing and future video adaptation algorithms in perspective with respect to user preferences. Our results show that the video quality can already be increased greatly by allowing few switches while more switches lead to diminishing gains. This is a novel discovery that is important for user-centric QoE-management which is of high interest for ISPs and video service providers.
Evaluation of Video Quality Monitoring based on Pre-computed Frame Distortions. Klein, Dominik; Zinner, Thomas; Borchert, Kathrin; Lange, Stanislav; Singeorzan, Vlad; Schmid, Matthias. In 19th EUNICE Workshop on Advances in Communication Networking, Best Paper Award. Chemnitz, Germany, 2013.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
- [ Download ]
A large fraction of the current Internet traffic is caused by video streaming. Due to the growing expectations of video consumers, monitoring video applications is getting more and more important for network and service providers. In a previous work, we proposed a video quality monitoring solution which utilizes the full reference SSIM metric to improve the monitoring in the network by distributing pre-computed distortion information induced by frame losses. To improve scalability, we introduced a less complex algorithm which infers the distortion for higher loss scenarios from single loss scenarios and inter-frame dependencies. In this work, we evaluate the accuracy of our algorithm by comparing it with the exact calculation of the SSIM metric for different frame loss scenarios. We further consider different high definition test video sequences and group of picture structures and investigate the influence on the accuracy of our proposed approximation.

@inproceedings{info3-inproceedings-2013-474, abstract = {A large fraction of the current Internet traffic is caused by video streaming. Due to the growing expectations of video consumers, monitoring video applications is getting more and more important for network and service providers. In a previous work, we proposed a video quality monitoring solution which utilizes the full reference SSIM metric to improve the monitoring in the network by distributing pre-computed distortion information induced by frame losses. To improve scalability, we introduced a less complex algorithm which infers the distortion for higher loss scenarios from single loss scenarios and inter-frame dependencies. In this work, we evaluate the accuracy of our algorithm by comparing it with the exact calculation of the SSIM metric for different frame loss scenarios. We further consider different high definition test video sequences and group of picture structures and investigate the influence on the accuracy of our proposed approximation.}, address = {Chemnitz, Germany}, author = {Klein, Dominik and Zinner, Thomas and Borchert, Kathrin and Lange, Stanislav and Singeorzan, Vlad and Schmid, Matthias}, booktitle = {19th EUNICE Workshop on Advances in Communication Networking, Best Paper Award}, keywords = {video-quality}, month = {08}, title = {Evaluation of Video Quality Monitoring based on Pre-computed Frame Distortions}, year = 2013 }

%0 Conference Paper %1 info3-inproceedings-2013-474 %A Klein, Dominik %A Zinner, Thomas %A Borchert, Kathrin %A Lange, Stanislav %A Singeorzan, Vlad %A Schmid, Matthias %B 19th EUNICE Workshop on Advances in Communication Networking, Best Paper Award %C Chemnitz, Germany %D 2013 %T Evaluation of Video Quality Monitoring based on Pre-computed Frame Distortions %X A large fraction of the current Internet traffic is caused by video streaming. Due to the growing expectations of video consumers, monitoring video applications is getting more and more important for network and service providers. In a previous work, we proposed a video quality monitoring solution which utilizes the full reference SSIM metric to improve the monitoring in the network by distributing pre-computed distortion information induced by frame losses. To improve scalability, we introduced a less complex algorithm which infers the distortion for higher loss scenarios from single loss scenarios and inter-frame dependencies. In this work, we evaluate the accuracy of our algorithm by comparing it with the exact calculation of the SSIM metric for different frame loss scenarios. We further consider different high definition test video sequences and group of picture structures and investigate the influence on the accuracy of our proposed approximation.

Video Quality Monitoring in Future Networks