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Decompiling QoE

This seminar explores the complex nature of QoE by identifying variables affecting it, characterizing their influence, and proposing a formalized approach for service providers.

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Decompiling QoE

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  1. Dagstuhl Seminar, Germany, January, 2015 Decompiling QoE Christos Tsiaras Department of Informatics IFI, Communication Systems Group CSG, University of Zürich UZH tsiaras@ifi.uzh.ch Background QoE Variables QoE Models Open questions

  2. Quality-of-Experience (QoE) • QoE is affected by multiple variables • Each variable has different importance • Each variable affects differently each user’s QoE • Each variable also affects the user differently in each service • The QoE concept is a mess! • Because is a user&service-centric concept • Mapping human brain into math is challenging • And fun 

  3. QoE in IPTV Services (Bandwidth, price) T. Hayashi, A. Takahashi, Nippon Telegraph and Telephone Corporation (NTT), Japan, "QoE Assessment Method for Video Quality and Pricing in IPTV Services", European Telecommunications Standards Institute (ETSI) Workshop, 17-19 June 2008, Prague, Czech Republic.

  4. QoE in VoIP (Latency) O3b Networks, Sofrecom, “Why Latency Matters to Mobile Backhaul”

  5. QoE in VoIP (Hops in WMNs) A. Chhabra, Grupal Singh, "Performance Evaluation and Delay Modeling of VoIP Traffic over 802.11 Wireless Mesh Network", International Journal of Computer Applications, Vol. 21, No. 9, pp. 0975 – 8887, May 2011.

  6. QoE in Video (IPTD & IPLR) 2/n-D S. Aroussi, T. Bouabana-Tebibel, A. Mellouk, "Empirical QoE/QoS correlation model based on multiple parameters for VoD flows," Global Communications Conference (GLOBECOM), 2012 IEEE, pp.1963-1968, 3-7 Dec. 2012

  7. QoE Models Market - IQX Hypothesis • 1 degree of freedom • β: curve gradient • α and γdefine the min and max MOS

  8. QoE Models Market – QoV • One degree of freedom per variable • β1, β2, ... • Describes multiple but only decreasing MOS • β0has to be defined based on the number of variables that are involved • The generic QoE equation do not reproduce the same QoE equation if only one QoS parameter is examined • Example that makes physics beautiful (Lorentz factor disappears)

  9. QoE Models Market – DQX (1) • 2 degrees of freedom • m: curve gradient • λd, QoS0: reference point • h and μ define the min and max MOS

  10. (Parenthesis) μ: the minimum score! All the eggs are not the same…The QoE concept was missingthe influence factor m in IQX h: width of the QoEMOS options λ: a reference point!(QoS,QoE)

  11. QoE Models Market – DQX (2) • Describes also increasing MOS • 2 degrees of freedom • m: curve gradient • λi, QoS0: reference point

  12. QoE Models Market – DQX (3) • Describes multiple mixed variables • Increasing/decreasing MOS • The multiple variables has an additional degree of freedom • wk: QoS importance factor

  13. QoE Models Market – DQX (4) • The generic equation boils down to the specific equation • The beauty of a generic model • Lorentz would be happy 

  14. Proposed Solution • Formalizing QoE in steps • Identify the variables that affect QoE • Characterize those variables • Increasing Variables (IVs) - The more you have the better it is • Decreasing Variables (DVs) - The more you have the worst it is • Select the ideal/desired/expected/agreed value of a variable • Considering the service specifications select the best and the worst values of the variable • Identify the effect of each variable’s variation • Influence factors • Identify the importance of each variable

  15. Example – Steps 1 and 2 • Scenario: Internet plans of an ISP for home customers in some places in Switzerland • Step 1: Variables identification • Uplink bandwidth • Downlink bandwidth • Price • Step 2: Variables characterization • IVs • Uplink bandwidth • Downlink bandwidth • DVs • Price

  16. Example – Step 3 • Step 3: Select the ideal/desired/expected/agreed value of a variable • Assume a customer selected the “Internet 50” option • Ideal values based on the SLA • Uplink bandwidth: 5 Mbit/s • Downlink bandwidth: 50 Mbit/s • Price: 59 CHF/month

  17. Example – Step 4 • Step 4: Select the best and worst values per variable • Best values • Uplink bandwidth: 15 Mbit/s • Downlink bandwidth: 250 Mbit/s • Price: 0 CHF/month • Worst values • Uplink bandwidth: 0.2 Mbit/s • Downlink bandwidth: 2 Mbit/s • Price: 89 CHF/month

  18. Example – Step 5 • Step 5: Identify the effect of each variable’s variation • When a customer is starting to get annoyed/getting pleased? • Estimate/Assume/Extract this information from the Customer Care department statistics about report of problems • E.g., 50% less than expected bandwidth dissatisfies a customer • E.g., 25% discount would satisfy a dissatisfied customer

  19. Example – Step 6 • Step 6: Identify the importance of each variable • How a customer selects a plan in this scenario? • Estimate/Assume/Extract through a survey: • 50% based on the price • 50% based on the downlink bandwidth

  20. Expected value Step 3 Influence factor Step 5 DQX Best and worst valuesStep 4 QoE equation for DVs Variables characterization Step 2 QoE equation for IVs Generic QoE equation Importance factor Step 6 QoE-related variables values Variables selection Step 1 QoE

  21. Is QoE Served?

  22. Open Question • Q: Is it possible to define importance factors (w) without: • Surveys • Curve fitting

  23. DQX in Practice • Mobile Network Performance • VoIP • Video streaming • BitTorrent • Browsing www.bonafide.pw

  24. Q&A Thank you!

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