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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

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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

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Why?

Tow main revenue models:

1.Subscription

2.Advertisement

advertisement

subscription

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Why?

Tow main revenue models:

1.Subscription

2.Advertisement

advertisement

subscription

The more you watch, The more we profit.

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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

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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

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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)

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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

…

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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

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Confounding factors-Identify

----3 round filter for all possible Con. Factors----

Round1: calculate Information Gain

Round2: compare Compacted Decision Tree

Round3: compare Tolerance

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Confounding factors-Identify

----3 round filter for all possible Con. Factors----

Round1: calculate Information Gain

Round2: compare Compacted Decision Tree

Round3: compare Tolerance

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Confounding factors-Identify

----3 round filter for all possible Con. Factors----

Round1: calculate Information Gain

Round2: compare Compacted Decision Tree

Round3: compare Tolerance

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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

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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！