%0 Conference Paper %1 info3-inproceedings-2017-3 %A Dinh-Xuan, Lam %A Seufert, Michael %A Wamser, Florian %A Tran-Gia, Phuoc %B 1st IFIP/IEEE International Workshop on Quality of Experience Management (QoE-Management) %C Lisbon, Portugal %D 2017 %K myown input can vnf_qoe_cloud %T Study on the Accuracy of QoE Monitoring for HTTP Adaptive Video Streaming Using VNF %X The fast growth of HTTP video streaming is responsible for a huge amount of traffic over the past few years. Due to the variety and popularity of video content, more and more people are watching videos on the smart TV or on mobile devices. As a result, a potential market is emerging for video providers, which can significantly increase their revenues. In order to offer users a good experience, adaptive video streaming has been introduced to adapt the video quality to the network conditions. Nevertheless, it is still difficult for the network operators to assess the actual video quality on the device of the users and therefore they can not react to improve the service on the network. In this work, we propose a Virtual Network Function (VNF) to monitor the Quality of Experience (QoE) for online video service in the network. To conduct the study, on the one hand, we design a VNF monitoring to measure the video quality and estimate the QoE at the client machine. Our function is placed in two locations nearby and far away from the user to analyze the impact of geographical placement of the VNF on its performance. On the other hand, we set up a local testbed to examine the functional operation and measure the actual video buffer from a client web browser directly to validate the accuracy of the function. Our findings show that with respect to function placement, the VNF has high accuracy in estimating the QoE if it is deployed at the edge network close to the user. However, the VNF does not perform well when it operates far away from the users, e.g., at data centers. These insights help network vendors to more closely monitor the quality of the videos streamed to their customers.

@inproceedings{info3-inproceedings-2017-3, abstract = {The fast growth of HTTP video streaming is responsible for a huge amount of traffic over the past few years. Due to the variety and popularity of video content, more and more people are watching videos on the smart TV or on mobile devices. As a result, a potential market is emerging for video providers, which can significantly increase their revenues. In order to offer users a good experience, adaptive video streaming has been introduced to adapt the video quality to the network conditions. Nevertheless, it is still difficult for the network operators to assess the actual video quality on the device of the users and therefore they can not react to improve the service on the network. In this work, we propose a Virtual Network Function (VNF) to monitor the Quality of Experience (QoE) for online video service in the network. To conduct the study, on the one hand, we design a VNF monitoring to measure the video quality and estimate the QoE at the client machine. Our function is placed in two locations nearby and far away from the user to analyze the impact of geographical placement of the VNF on its performance. On the other hand, we set up a local testbed to examine the functional operation and measure the actual video buffer from a client web browser directly to validate the accuracy of the function. Our findings show that with respect to function placement, the VNF has high accuracy in estimating the QoE if it is deployed at the edge network close to the user. However, the VNF does not perform well when it operates far away from the users, e.g., at data centers. These insights help network vendors to more closely monitor the quality of the videos streamed to their customers.}, added-at = {2017-01-31T09:30:09.000+0100}, address = {Lisbon, Portugal}, author = {Dinh-Xuan, Lam and Seufert, Michael and Wamser, Florian and Tran-Gia, Phuoc}, biburl = {https://www.bibsonomy.org/bibtex/24603d921c48aa306cc0f5809f60968d2/uniwue_info3}, booktitle = {1st IFIP/IEEE International Workshop on Quality of Experience Management (QoE-Management)}, interhash = {df96d47b0833df91ae0302d2aa737599}, intrahash = {4603d921c48aa306cc0f5809f60968d2}, keywords = {myown input can vnf_qoe_cloud}, month = {5}, timestamp = {2022-03-14T00:08:48.000+0100}, title = {Study on the Accuracy of QoE Monitoring for HTTP Adaptive Video Streaming Using VNF}, year = 2017 }