Network-Based Monitoring of Quality of Experience

Abstract: The recent years have observed a tremendous shift from the technology-centric assessment to the user-centric assessment of network services. Consequently, measurement and modelling of Quality of Experience (QoE) attracted many contributions from researchers and practitioners. Generally, QoE is assessed via active and passive measurements. While the former usually allows QoE assessment on the test traffic, the latter opens avenues for continuous QoE assessment on the real traffic generated by the users. This thesis contributes towards passive assessment of QoE.This thesis document begins with a background on the fundamentals of network management and objective QoE assessment. It extends the discussion further to the QoE-centric monitoring and management of network, complimented by the details about QoE estimator agent developed within the Celtic project QuEEN (Quality of Experience Estimators in Network).The discussion on findings starts with results from subjective tests to understand the relationship between waiting times and user subjective feedback over time. These results strengthen the understanding of timescales on which users react, as well as, the effect of user memory on QoE. The findings show that QoE drops significantly when the user faces recurring waiting times of 0.5 s to 4 s durations in case of video streaming and web browsing services. With recurring network disturbances within every 8 s – 16 s time intervals, the user tolerance to waiting times decreases constantly, showing the sign of user memory of recent disturbances.Subsequently, this document introduces and evaluates a passive wavelet-based QoE monitoring method. The method detects timescales on which transient outages occur frequently. A study presents results from qualitative measurements, showing the ability of wavelet to differentiate on-fly between “Good” and “Bad” traffic streams. In sequel, a quantitative study systematically evaluates wavelet-based metrics. Subsequently, the subjective evaluation and wavelet analysis of 5 – 6 minutes long video streaming sessions on mobile networks show that wavelet-based metrics is indeed useful for passive monitoring of QoE issues.Finally, this thesis investigates a method for passive monitoring of user reactions to degrading network performance. The method is based on the TCP termination flags. With a systematic evaluation in a test environment, the results characterise termination of data transfers in case of different user actions in the web browser.