Study on the accuracy of QoE monitoring for HTTP adaptive video streaming using VNF

The fast growth of video streaming offers a potential market for video providers, which can significantly increase their revenues. In order to provide users a good experience, HTTP 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 may 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. On the other hand, we place the function in two locations nearby and far away from the user to analyze the impact of geographical placement of the VNF on its performance. 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.