1 developing a predictive model of quality of experience for internet video athula balachandran...
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1
Developing a Predictive Model ofQuality of Experience for Internet Video
Athula Balachandran
Carnegie Mellon University
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Content
1 Why do quality of experience(QoE)?
2 Where are the challenges?
3 How to do ?
4 Implication and evaluation
3
Content
11 Why do quality of experience(QoE)? 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)?
22 Where are the challenges? 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)
<average bitrate, join time, buffering ration, rate of buffering>
■ User experience: User Opinion Scores
User’s Engagement-centric( viewing time , number of visits)
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Era changed & Requirement improve
■Video quality: PSNR(Peak Signal-to-Noise Ration)
<average bitrate, join time, buffering ration, rate of buffering>
■ 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
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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
…
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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 engagement2.Complex relationship
bitrate
Join time
Visits num
Viewing time
…
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Challenge 3 confound factors
Confound factors affect quality -> engagement
3.Confound factors influence
Type of Device
Type of video
…
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Content
1 Why do quality of experience(QoE)?
2 Where are the challenges?
33 How to do ? How to do ?
4 Implication and evaluation
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Compare current work
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Compare current work
1.Model consider complex relationship and confound factors
2.Provide strategy for system design
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Requirements for predictive model
Tackling relationship (quality->engagement) and interdependency (among quality)
Tackling confounding factors 1.Identifying the import confounding factors2.Address the confounding factors
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Compare methods for tackling relation
Compare the accuracy of tackling relationship( quality -> engagement) and interdependency (among quality)
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Confounding factors-Identify
----3 round filter for all possible Con. Factors----
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Confounding factors-Identify
----3 round filter for all possible Con. Factors----
Round1: calculate Information Gain
Entropy:
Condition entropy:
Information gain:
Relative Information gain:
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Confounding factors-Identify
----3 round filter for all possible Con. Factors----
Round1: calculate Information Gain
Entropy:
Condition entropy:
Information gain:
Relative Information gain:
……
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Confounding factors-Identify
----3 round filter for all possible Con. Factors----
Round1: calculate Information Gain
Round2: compare Compacted Decision Tree
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Round2: compare Compacted Decision Tree
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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<8.5,A2>1.8,A2<=2.5 ○ [sup=14%,con=100%]
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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<8.5,A2>1.8,A2<=2.5 ○ [sup=14%,con=100%]
■ ○ Total
A1<=4 24 0 24
A1 > 4 6.45 29.55 36
Total 30.45 29.55 60
χ2(A1<=4 ■) = (24-30.45)^2/30.45 = 1.37χ2(A1>4 ○) = (29.55-29.55)^2/29.55 = 0
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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<8.5,A2>1.8,A2<=2.5 ○ [sup=14%,con=100%]
■ ○ Total
A1>4,A2<=2.5,A1>7 6.45 1.05 7.5
A1>4,A2<=2.5,A1<=7 0 7.5 7.5
Total 6.45 8.55 15
χ2(..A1>7 ■) = (6.45-6.45)^2/6.45 = 0χ2(..A1<=7 ○) = (7.05-8.55)^2/8.55 = 0.26
1.Dif 2.sig 3. n(current) >= n(former) +1
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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<8.5,A2>1.8,A2<=2.5 ○ [sup=14%,con=100%]
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Confounding factors-Identify
----3 round filter for all possible Con. Factors----
Round1: calculate Information Gain
Round2: compare Compacted Decision Tree
29
Confounding factors-Identify
----3 round filter for all possible Con. Factors----
Round1: calculate Information Gain
Round2: compare Compacted Decision Tree
Round3: compare Tolerance
30
Confounding factors-Identify
----3 round filter for all possible Con. Factors----
Round1: calculate Information Gain
Round2: compare Compacted Decision Tree
Round3: compare Tolerance
31
Confounding factors-Identify
----3 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:
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Confounding factors-Address
Compare tow candidate way:
Add as a new feature
Split data by Con. factors
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Content
1 Why do quality of experience(QoE)?
2 Where are the challenges?
3 How to do ?
44 Implication and evaluation Implication and evaluation
35
Implication for system design
For an example model:buffering ratio, rate of buffering,
join time
Estimate all possible combinations
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Implication for system design
Thanks！
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