Ying Wai Wong, Jack Y. B. Lee, Victor O. K. Li, and Gary S. H. Chan

CSVT 2007 FEB

Supporting Interactive Video-on-Demand With Adaptive Multicast

Streaming

IntroductionMulticast StreamingInteractive Playback SupportInteractive Multicast StreamingStatic Full Stream Scheduling (SFSS)Adaptive Full Stream SchedulingPerformance Evaluation

Multicast StreamingConcept

Video Server

Player A

Player B

Player C

10:00

10:00

10:03

Player D10:05

Tcsu (http://vc.cs.nthu.edu.tw/home/paper/codfiles/tcsu/200104221412/odmr_jswang.ppt) time10:00 10:03 10:05

Stream 1 Stream 2 Stream 3

Multicast StreamingCaching and Patching

Caching and Sharing the streams in clientsThe missed initial portion is transmitted by

patchingFull Stream

Patching

Caching in cb

Caching in cc

Caching in cb

Caching in cc

time time

Partial Stream

Multicast StreamingControlled greedy recursive patching

(CGRA) [9]A new client caches data from the latest

reachable streamCost-aware recursive patching (CARP) [9]

A new client is inserted in the merge treeDyadic [13]

Dyadic interval: t + L/(2ri)Earliest Reachable Merge Target (ERMT)

[10]

time

time

time

time

Time to wait before playback beginning

Interactive Playback SupportUnicast

Dedicated streamMulticast

Discontinuous interactive playbackStaggered multicast video streaming [30]Split-and-Merge (Dedicated interactive stream) [28]

Best-Effort Patching (cache from more than one stream) [33]

6 7 8… …current

Jump point

stream

pktmtimeCurrent point

Stream 1

Stream 2

Stream 3

Stream 4

Dedicated streampk-tm

Interactive Multicast StreamingIssues

Interactivity Model Request Scheduling Client Buffer Management

Interactive Multicast StreamingInteractivity Model

Interactive operations (VCR operations)Pause/resume, slow motion, frame stepping, fast

forward/backward visual search, and forward/backward seeking

Multi-campus interactive educational resource system [37]Exponential distribution Not suitable for entertainment contents

Two state model – NORMAL and INTERACTION [36]Exponentially distributed staying in one state

Multi-state model [31]

Interactive Multicast StreamingRequest Scheduling

Admission requestsGenerated by new clients

Merging requestsGenerated by clients performing VCR

ImportanceMerging requests > Admission requests

Full-stream restart threshold W

W Wtime

time

Partial stream (Merging requests)

time

Full stream (admission requests)

Interactive Multicast StreamingClient Buffer Management

Playback rate: R bpsReceiving rate: 2R bpsBuffer accumulation rate: R bpsMinimum required buffer size: WR bitsAssume maximum buffer size is limited to

BcR bitsW Bc

(pk – tm) Bc

Tp + Tpc BcPAUSE duration Patching and caching duration

Wtime

Caching

Maximum buffer time

Nearest playback point after VCR operations

VCR duration

Interactive Multicast StreamingPerformance Impact

Time to wait before playback beginning

Time to wait from VCR to playback resuming

# of server channels: 10Probability of FSEEK:0.1

Interactive Multicast StreamingPerformance Impact (2)

# of server channels: 24Probability of FSEEK:0.03Probability of BSEEK:0.03Probability of PAUSE:0.03

Static Full Stream Scheduling (SFSS)Assumption:

Full streams are generated every W secondsMerging request rate: uVCR requests/second

# of full streams in the system: L/WAverage playback point after VCR operations before

next full stream: W/2Average merging cost: WR/2Average system cost: uVCR (WR/2)Resource consumption rate of full stream: RL/WTotal resource consumption rate: uVCR (WR/2) + RL/W

When W = (2L/uVCR)1/2, we have the minimum consumption rate

W Wtime

…

W is a decreasing function of uVCR

Partial stream

FSEEK,BSEEK,PAUSE

Adaptive Full Stream SchedulingAn optimal W must be found with knowing all the

system parameters.Five system parameters

Client arrival rate , Probability of FSEEK Pf, Probability of BSEEK Pb, Probability of PAUSE Pp, Average seek distance sd

1.Initial full-stream restart threshold (W) is needed such that system parameters can be measured.

2.Embedded simulator is applied to find the optimal threshold by the measured parameters.

3.The changes of the system parameters are detected and go to step 2.

%2

CI%95K

95% confidence interval

Performance EvaluationOptimization of the Full Stream Restart

Threshold

# of server channels: 24Probability of FSEEK:0.1Probability of BSEEK:0.1Probability of PAUSE:0.1

Performance EvaluationLatencies Comparisons

Improvement: 98%

Performance EvaluationLatencies Comparisons

Improvement: 90%

# of server channels: 24Probability of FSEEK:0.05Probability of BSEEK:0.05Probability of PAUSE:0.05

Performance EvaluationEffect of Client Buffer Constraint

L

Performance EvaluationJust-in-Time Simulation (Adaptive W)

Performance EvaluationJust-in-Time Simulation (Adaptive W)

Performance EvaluationAdaptive FSS vs. Static FSS