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

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Presentation on theme: "1 Developing a Predictive Model of Quality of Experience for Internet Video Athula Balachandran Carnegie Mellon University."— Presentation transcript:

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

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

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

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

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

6 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 7 Content 1 Why do quality of experience(QoE)? 2 2 Where are the challenges? 3 How to do ? 4 Implication and evaluation

8 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 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 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 11 Challenge 1 quality interdependence Among video quality are subtle interdependence 1.Video quality interdependence bitrate Join time bufratio …

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

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

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

15 15 Compare current work

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

17 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 18 Compare methods for tackling relation Compare the accuracy of tackling relationship( quality -> engagement) and interdependency (among quality)

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

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

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

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

23 23 Round2: compare Compacted Decision Tree

24 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 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 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 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 28 Confounding factors-Identify round filter for all possible Con. Factors---- Round1: calculate Information Gain Round2: compare Compacted Decision Tree

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

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

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

32 32 Confounding factors-Address Compare tow candidate way:

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

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

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

36 36 Implication for system design

37 Thanks !


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