1. Dagstuhl Seminar, Germany, January, 2015
Decompiling QoE
Christos Tsiaras
Department of Informatics IFI, Communication Systems Group CSG, University of Zürich UZH
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 
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!

3. 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, June 2008, Prague, Czech Republic.

4. O3b Networks, Sofrecom, “Why Latency Matters to Mobile Backhaul”
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 Wireless Mesh Network", International Journal of Computer Applications, Vol. 21, No. 9, pp – 8887, May 2011.

6. 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 , 3-7 Dec. 2012

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

8. 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

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
Προσημο=sign

10. (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

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
DVs

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. 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

21. Is QoE Served?

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

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

24. Q&A
Thank you!