piwik-script

Deutsch 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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