1 Developing a Predictive Model of Quality of Experience for Internet Video Athula Balachandran Carnegie Mellon University

2 Content 1 Why do quality of experience(QoE)? 2 Where are the challenges? 3 How to do ? 4 Implication and evaluation

3 Content 1 1 Why do quality of experience(QoE)? 2 Where are the challenges? 3 How to do ? 4 Implication and evaluation

4 Why? Tow main revenue models: 1.Subscription 2.Advertisement advertisement subscription

5 Why? Tow main revenue models: 1.Subscription 2.Advertisement advertisement subscription The more you watch, The more we profit.

6 Why? Tow main revenue models: 1.Subscription 2.Advertisement advertisement subscription The more you watch, The more we profit. Improving users’ quality of experience(QoE) is crucial

7 Content 1 Why do quality of experience(QoE)? 2 2 Where are the challenges? 3 How to do ? 4 Implication and evaluation

8 Era changed & Requirement improve ■ Video quality: PSNR(Peak Signal-to-Noise Ration) ■ User experience: User Opinion Scores User’s Engagement-centric( viewing time, number of visits)

9 Era changed & Requirement improve ■ Video quality: PSNR(Peak Signal-to-Noise Ration) ■ User experience: User Opinion Scores User’s Engagement-centric( viewing time, number of visits)  Average bitrate: HD(High-Definition) SD(Standard-Definition) LD(Low- Definition)  Join time: load time  Buffering ratio: buffer_time/(buffer_time+play_time)  Rate of buffering: frequency of buffering

10 Challenge scope Video quality User engagement 1.Video quality interdependence 2.Complex relationship 3.Confound factors influence bitrate Join time bufratio … Visits num Viewing time … Time of day Type of video …

11 Challenge 1 quality interdependence Among video quality are subtle interdependence 1.Video quality interdependence bitrate Join time bufratio …

12 Challenge 2 complex relationship Relationship between quality and engagement 2.Complex relationship bitrate Join time Visits num Viewing time …

13 Challenge 3 confound factors Confound factors affect quality -> engagement 3.Confound factors influence Type of Device Type of video …

14 Content 1 Why do quality of experience(QoE)? 2 Where are the challenges? 3 3 How to do ? 4 Implication and evaluation

15 Compare current work

16 Compare current work 1.Model consider complex relationship and confound factors 2.Provide strategy for system design

17 Requirements for predictive model  Tackling relationship (quality->engagement) and interdependency (among quality)  Tackling confounding factors 1. Identifying the import confounding factors 2.Address the confounding factors

18 Compare methods for tackling relation Compare the accuracy of tackling relationship( quality -> engagement) and interdependency (among quality)

19 Confounding factors-Identify round filter for all possible Con. Factors----

20 Confounding factors-Identify round filter for all possible Con. Factors---- Round1: calculate Information Gain Entropy: Condition entropy: Information gain: Relative Information gain:

21 Confounding factors-Identify round filter for all possible Con. Factors---- Round1: calculate Information Gain Entropy: Condition entropy: Information gain: Relative Information gain: … …

22 Confounding factors-Identify round filter for all possible Con. Factors---- Round1: calculate Information Gain Round2: compare Compacted Decision Tree

23 Round2: compare Compacted Decision Tree

24 Round2: compare Compacted Decision Tree GE-1: A1<=4  ■ [sup=40%,con=100%] GE-2:A1>4  ○ [sup=60%,con=82%] Except: A1>7,A2<=2.5  ■ [sup=20%,con=86%] Except:A1 1.8,A2<=2.5  ○ [sup=14%,con=100%]

25 Round2: compare Compacted Decision Tree GE-1: A1<=4  ■ [sup=40%,con=100%] GE-2:A1>4  ○ [sup=60%,con=82%] Except: A1>7,A2<=2.5  ■ [sup=20%,con=86%] Except:A1 1.8,A2<=2.5  ○ [sup=14%,con=100%] ■○ Total A1<=4240 A1 > Total χ2(A1<=4  ■ ) = ( )^2/30.45 = 1.37 χ2(A1>4  ○ ) = ( )^2/29.55 = 0

26 Round2: compare Compacted Decision Tree GE-1: A1<=4  ■ [sup=40%,con=100%] GE-2:A1>4  ○ [sup=60%,con=82%] Except: A1>7,A2<=2.5  ■ [sup=20%,con=86%] Except:A1 1.8,A2<=2.5  ○ [sup=14%,con=100%] ■○ Total A1>4,A A1>4,A2<=2.5,A1<=707.5 Total χ2(..A1>7  ■ ) = ( )^2/6.45 = 0 χ2(..A1<=7  ○ ) = ( )^2/8.55 = Dif 2.sig 3. n(current) >= n(former) +1

27 Round2: compare Compacted Decision Tree GE-1: A1<=4  ■ [sup=40%,con=100%] GE-2:A1>4  ○ [sup=60%,con=82%] Except: A1>7,A2<=2.5  ■ [sup=20%,con=86%] Except:A1 1.8,A2<=2.5  ○ [sup=14%,con=100%]

28 Confounding factors-Identify round filter for all possible Con. Factors---- Round1: calculate Information Gain Round2: compare Compacted Decision Tree

29 Confounding factors-Identify round filter for all possible Con. Factors---- Round1: calculate Information Gain Round2: compare Compacted Decision Tree Round3: compare Tolerance

30 Confounding factors-Identify round filter for all possible Con. Factors---- Round1: calculate Information Gain Round2: compare Compacted Decision Tree Round3: compare Tolerance

31 Confounding factors-Identify round filter for all possible Con. Factors---- Round1: calculate Information Gain Round2: compare Compacted Decision Tree Round3: compare Tolerance

32 Confounding factors-Address Compare tow candidate way:

33 Confounding factors-Address Compare tow candidate way: Add as a new feature Split data by Con. factors

34 Content 1 Why do quality of experience(QoE)? 2 Where are the challenges? 3 How to do ? 4 4 Implication and evaluation

35 Implication for system design For an example model: buffering ratio, rate of buffering, join time Estimate all possible combinations

36 Implication for system design

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