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

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  Mapping the human brain in to math is hard. So lets leave it to God On the other hand, how bad can it be? Lets give it a try!

QoE in IPTV Services (Bandwidth, price) WiKi: NTT is the largest telecommunications company in the world in terms of revenue 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.

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

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.

QoE in Video (IPTD & IPLR) 2/n-D IPTD=IP packet Transfer Delay IPLR=IP packet Loss Ratio 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

QoE Models Market - IQX Hypothesis 1 degree of freedom β: curve gradient α and γ define the min and max MOS

QoE Models Market – QoV One degree of freedom per variable β1, β2, ... Describes multiple but only decreasing MOS β0 has 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) Not a nice property Imagine if relativistic mass equation would not measure the same mass if the speed of an object would be less than the speed of light Lorentz factor

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 Προσημο=sign

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

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

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

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

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

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 DVs

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

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

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

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

DQX Expected value Influence factor Step 3 Step 5 QoE equation for DVs Best and worst values Step 4 QoE-related variables values Variables characterization Step 2 QoE QoE equation for IVs Generic QoE equation Importance factor Step 6 4. Considering the service specifications select the best and the worst values of the variable Step 6 is another degree of freedom to calibrate the model in a better way. You can start by setting the importance factor = 1 Variables selection Step 1

Is QoE Served?

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

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

Q&A Thank you!