an efficient implementation of interactive video-on-demand steven carter and darrell long...

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An Efcient Implementation of Interactive Video-on- Demand Steven Carter and Darrell Long University of California, Santa Cruz Jehan-François Pâris University of Houston

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Page 1: An Efficient Implementation of Interactive Video-on-Demand Steven Carter and Darrell Long University of California, Santa Cruz Jehan-François Pâris University

An Efficient Implementation of Interactive Video-on-Demand

Steven Carter and Darrell LongUniversity of California, Santa Cruz

Jehan-François PârisUniversity of Houston

Page 2: An Efficient Implementation of Interactive Video-on-Demand Steven Carter and Darrell Long University of California, Santa Cruz Jehan-François Pâris University

Why Video-on-Demand? Increased customer convenience

Few people enjoy returning video tapes Even fewer people enjoy paying late fees

Improved selection of videos Current pay-per-view provides only a small selection

of popular videos

Savings in time and resources It takes time and fuel to drive to the video rental store

Page 3: An Efficient Implementation of Interactive Video-on-Demand Steven Carter and Darrell Long University of California, Santa Cruz Jehan-François Pâris University

Why Now? Technology becoming available

Processors are inexpensive Storage is nearly free ($200 for 40GB) Fast networking is seeing wide deployment

Consider the success of Tivo Records live television using MPEG to disk Provides interactive access to recorded

programs

Page 4: An Efficient Implementation of Interactive Video-on-Demand Steven Carter and Darrell Long University of California, Santa Cruz Jehan-François Pâris University

Why Interactive? It’s hard! It’s more expensive!

… but it’s what people expect

They won’t give up functionality they have come to expect They’d like to pause to make microwave popcorn They’d like to rewind to see the play again They’d like to be able to fast forward past the boring

parts

Page 5: An Efficient Implementation of Interactive Video-on-Demand Steven Carter and Darrell Long University of California, Santa Cruz Jehan-François Pâris University

Related Research Conventional video-on-demand (VoD)

Requires one stream per client

Patching An independently developed version of stream

tapping

Batching Group the requests of several clients together

Various near video-on-demand (NVoD) schemes

Page 6: An Efficient Implementation of Interactive Video-on-Demand Steven Carter and Darrell Long University of California, Santa Cruz Jehan-François Pâris University

Key Observation For videos of non-trivial length, several

clients will be viewing portions of that video One client watching a 120 minute video and a

second client begins watching the same video 10 minutes later

The server needs only send data for the non-overlapping portion The potential for savings is enormous

Page 7: An Efficient Implementation of Interactive Video-on-Demand Steven Carter and Darrell Long University of California, Santa Cruz Jehan-François Pâris University

Assumptions A set-top-box with:

A fast network connection A few gigabytes of local storage A modest processor

Keep in mind that set top boxes with these features already exist

Page 8: An Efficient Implementation of Interactive Video-on-Demand Steven Carter and Darrell Long University of California, Santa Cruz Jehan-François Pâris University

Our Solution Stream Tapping uses multicast to tap in existing

video streams Server load is the primary difficult in making

VOD a reality Stream Tapping reduces server load by allowing

clients to tap into video streams created for other clients

Cost per client is dramatically reduced

Client waiting time is also reduced

Page 9: An Efficient Implementation of Interactive Video-on-Demand Steven Carter and Darrell Long University of California, Santa Cruz Jehan-François Pâris University

Stream Types

A

B

C

0 2 3 4

Str

eam

Time (since start of complete stream A)

b

c c

b

Full tap

Complete stream

Partial tap

Page 10: An Efficient Implementation of Interactive Video-on-Demand Steven Carter and Darrell Long University of California, Santa Cruz Jehan-François Pâris University

Complete Streams Start at a particular position in a video and

transmit the remainder of the video For non-interactive Stream Tapping, the

starting position is the beginning of the video Used primarily by the first client in a group

to view the video

Page 11: An Efficient Implementation of Interactive Video-on-Demand Steven Carter and Darrell Long University of California, Santa Cruz Jehan-François Pâris University

Stream Types

A

B

C

0 2 3 4

Str

eam

Time (since start of complete stream A)

b

c c

b

Full tap

Complete stream

Partial tap

Page 12: An Efficient Implementation of Interactive Video-on-Demand Steven Carter and Darrell Long University of California, Santa Cruz Jehan-François Pâris University

Full Tap Streams Can be used if the delay () is less than the

buffer size () The full tap stream transmits the video

from time 0 to The complete stream is tapped and written

to the buffer while the full tap stream is played

Page 13: An Efficient Implementation of Interactive Video-on-Demand Steven Carter and Darrell Long University of California, Santa Cruz Jehan-François Pâris University

Stream Types

A

B

C

0 2 3 4

Str

eam

Time (since start of complete stream A)

b

c c

b

Full tap

Complete stream

Partial tap

Page 14: An Efficient Implementation of Interactive Video-on-Demand Steven Carter and Darrell Long University of California, Santa Cruz Jehan-François Pâris University

Partial Tap Streams Can be used when a complete stream is available

but Note that given current technology, will be very

large The client will tap the complete stream for units

while simultaneously viewing the first from a partial tap stream

Subsequently, partial tap streams of length are used for the client to catch up to the complete stream

Page 15: An Efficient Implementation of Interactive Video-on-Demand Steven Carter and Darrell Long University of California, Santa Cruz Jehan-François Pâris University

Stream Types

A

B

C

0 2 3 4

Str

eam

Time (since start of complete stream A)

b

c c

b

Full tap

Complete stream

Partial tap

Page 16: An Efficient Implementation of Interactive Video-on-Demand Steven Carter and Darrell Long University of California, Santa Cruz Jehan-François Pâris University

Tapping Options Extra Tapping

Allows the client to tap data from any active video stream active, not just the complete stream of the video group

Decreases server load by decreasing the length of full tap streams

Stream Stacking If the server has streams available, the client can combine

them to receive data at rate higher than the nominal rate Allows the server to service stream more quickly, which

allows new streams to be scheduled

Page 17: An Efficient Implementation of Interactive Video-on-Demand Steven Carter and Darrell Long University of California, Santa Cruz Jehan-François Pâris University

Interactive Stream Tapping When an interaction begins, Stream Tapping

deallocates resources associated with a client If the client was the only one using a stream, then the

stream is terminated Stream Tapping determines the resources needed for

an interaction, and allocates an interaction stream Note: for rewind, the client’s buffer can be used

When the interaction is complete, the client is merged into a video group (tapping existing streams if available)

Page 18: An Efficient Implementation of Interactive Video-on-Demand Steven Carter and Darrell Long University of California, Santa Cruz Jehan-François Pâris University

Contingency Streams These are streams that are held in reserve

for interaction The pool of these streams can be managed

using high and low watermarks for hysteresis

Having such a reserve of streams is essential to avoid blocking

Page 19: An Efficient Implementation of Interactive Video-on-Demand Steven Carter and Darrell Long University of California, Santa Cruz Jehan-François Pâris University

Simulation Model Stream Tapping is too complex to model

analytically, so we used discrete event simulation The length of the videos was derived from

empirical data and a gaussian with mean 102 minutes provided the best fit

The popularity of videos was modeled using a Zipf-like distribution, which is the distribution used in most VoD studies

Page 20: An Efficient Implementation of Interactive Video-on-Demand Steven Carter and Darrell Long University of California, Santa Cruz Jehan-François Pâris University

Comparison with Conventional Systems

0

100

200

300

400

500

600

700

0 50 100 150 200 250 300 350 400

Arrival Rate

Ban

dw

idth

Conventional System

Staggered Broadcasting (2 min)

Staggered Broadcasting (5 min)

Stream Tapping

Page 21: An Efficient Implementation of Interactive Video-on-Demand Steven Carter and Darrell Long University of California, Santa Cruz Jehan-François Pâris University

Contingency Streams versus Start-up Latency

0

5

10

15

20

25

30

35

40

45

0 10 20 30 40 50 60 70 80 90 100

Contingency Streams

Av

era

ge

Sta

rt-u

p L

ate

nc

y (

min

)

Page 22: An Efficient Implementation of Interactive Video-on-Demand Steven Carter and Darrell Long University of California, Santa Cruz Jehan-François Pâris University

Contingency Streams versus Resume Latency

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

0 10 20 30 40 50 60 70 80 90 100

Contingency Streams

Ave

rag

e S

tart

-up

Lat

ency

(m

in)

Page 23: An Efficient Implementation of Interactive Video-on-Demand Steven Carter and Darrell Long University of California, Santa Cruz Jehan-François Pâris University

Contingency Streams versus Blocking Probability

0

10

20

30

40

50

60

70

80

0 10 20 30 40 50 60 70 80

Contingency Streams

Inte

racti

on

Blo

ckin

g P

rob

ab

ilit

y (

%)

Page 24: An Efficient Implementation of Interactive Video-on-Demand Steven Carter and Darrell Long University of California, Santa Cruz Jehan-François Pâris University

Effect of Client Buffer Size

0

20

40

60

80

100

120

140

0 10 20 30 40 50 60

STB Buffer Size (min)

Ac

era

ge

Sta

rt-u

p L

ate

nc

y (m

in)

Page 25: An Efficient Implementation of Interactive Video-on-Demand Steven Carter and Darrell Long University of California, Santa Cruz Jehan-François Pâris University

Effect of Tapping Options

0

10

20

30

40

50

60

70

80

0 10 20 30 40 50 60

STB Buffer Size (min)

Av

era

ge

Sta

rt-u

p L

ate

nc

y (

min

)

NeitherStream Stacking OnlyExtra TappingBoth

Page 26: An Efficient Implementation of Interactive Video-on-Demand Steven Carter and Darrell Long University of California, Santa Cruz Jehan-François Pâris University

Conclusions Stream Tapping has been shown to work

well in the interactive environment We have shown that VCR-like controls are

possible Previous work has ignored them or only

provided course-grained control The use of storage in the STB is an

enabling technology

Page 27: An Efficient Implementation of Interactive Video-on-Demand Steven Carter and Darrell Long University of California, Santa Cruz Jehan-François Pâris University

Video Length Distribution