piwik-script

Intern
Lehrstuhl für Informatik III

Project: H2020 Mobi-QoE

Mobi-QoE (Monitoring and Analysis of Quality of Experience in Mobile Broadband Networks, funded by European Commission H2020 project MONROE (Measuring Mobile Broadband Networks in Europe))

The objective of Mobi-QoE is to extend MONROE’s testbed to the QoE domain by integrating novel software-based QoE-capable measurement tools and QoE models for popular end-user services (e.g., YouTube, Facebook, Spotify). In crowdsourced field trials, the extensions will be evaluated and the QoE models will be refined to enable experiments for QoE-based performance analysis of mobile broadband networks with the MONROE testbed.

The project Mobi-QoE is conducted in collaboration with AIT Austrian Institute of Technology GmbH

Researchers:

Dr. Florian Wamser,
Anika Schwind M.Sc.

Pedro Casas,
Dr. Michael Seufert

Tool: YoMo-Docker

Yomo-Docker is a Docker container to actively measure QoE related factors of YouTube video streaming. The measurement concept is based on emulating a virtual end-user device requesting video streams, which are then monitored at the network and application layers, on the basis of QoE-relevant features.

It is available on DockerHub by pulling mobiqoe/yomo_docker or on GitHub in the yomo-docker repository.

For more information, please contact Anika Schwind (anika.schwind@informatik.uni-wuerzburg.de).

Local Testing

To start local QoE tests, Docker has to be installed. Then, simply run the following command and get the results into a selected folder:

docker run --cap-add=NET_ADMIN --env LOCAL=1 -v <Path to result folder>:/monroe/results yomo_docker

or use a config file in addition to specify YouTube ID, duration and bitrates for different quality levels:

docker run --cap-add=NET_ADMIN --env LOCAL=1 -v <Path to config file>:/monroe/config -v <Path to result folder>:/monroe/results yomo_docker


Output

The container will export three different log files:

  1. Information about the playout buffer (in intervals of approx. 1s) in the following format:
    timestamp#video playback time#buffered playback time#available playback time\n
  2. Information about the video player (playback events, video information) in the following format:
    timestamp#information\n
  3. Statistics about bitrate, buffer, and stallings
    avg, max, min, 25-50-75-90 quantiles of: bitrate [KB], buffer [s], number of stalls (only one value), duration of stalls
  4. Network traffic information during the video playback using tshark

Publications

2018

  • Schwind, A., Wamser, F., Gensler, T., Seufert, M., Casas, P., Tran-Gia, P.: Streaming Characteristics of Spotify Sessions.The 2nd International Workshop on Quality of Experience Management. , Sardinia, Italy (2018).
     

2017

  • Seufert, M., Wehner, N., Wamser, F., Casas, P.: YouTube QoE Monitoring with YoMoApp: a Mobile App for Crowdsourced YouTube QoE Analysis, (2017).
     
  • Schwind, A., Seufert, M., Alay, Ö., Casas, P., Tran-Gia, P., Wamser, F.: Concept and Implementation of Video QoE Measurements in a Mobile Broadband Testbed.IEEE/IFIP Workshop on Mobile Network Measurement (MNM’17). , Dublin, Ireland (2017).
     
  • Seufert, M., Zach, O., Slanina, M., Tran-Gia, P.: Unperturbed Video Streaming QoE Under Web Page Related Context Factors.9th International Conference on Quality of Multimedia Experience (QoMEX). , Erfurt, Germany (2017).
     
  • Seufert, M., Wehner, N., Wamser, F., Casas, P., D'Alconzo, A., Tran-Gia, P.: Unsupervised QoE Field Study for Mobile YouTube Video Streaming with YoMoApp.9th International Conference on Quality of Multimedia Experience (QoMEX). , Erfurt, Germany (2017).
     

Student Works and Theses

2018

  • Gensler, T.: Concept an Implementation of QoE Measurements for Audio Streaming in Spotify, (2018).
     

2017

  • Seufert, M., Wehner, N., Wamser, F., Casas, P.: YouTube QoE Monitoring with YoMoApp: a Mobile App for Crowdsourced YouTube QoE Analysis, (2017).
     
  • Wehner, N.: Unsupervised QoE Field Study for Mobile YouTube Video Streaming with YoMoApp, (2017).
     
  • Schwind, A.: Concept and Implementation of QoE Measurements in a Mobile Broadband Testbed, (2017).
     

2016

  • Zeidler, B.: Comparison of Machine Learning Approaches for YouTube Video Adaptation Estimation on Encrypted Traffic, (2016).
     

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