large-scale and cost-effective video services
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Large-Scale and Cost-Effective Video Services. CS587x Lecture Department of Computer Science Iowa State University. Contents. On-demand Multicast Periodic Broadcast Application-Layer Multicast Peer-to-Peer Video Services. What to Cover Today. On-demand Multicast Patching - PowerPoint PPT PresentationTRANSCRIPT
Large-Scale and Cost-Effective Video Services
CS587x LectureDepartment of Computer Science
Iowa State University
• On-demand Multicast
• Periodic Broadcast
• Application-Layer Multicast
• Peer-to-Peer Video Services
Contents
• On-demand Multicast
– Patching
– Double Patching
• Periodic Broadcast
– Client Centric Approach
What to Cover Today
Server Channels
• Videos are delivered to clients as a continuous stream.
• Server bandwidth determines the number of video streams can be supported simultaneously.
• Server bandwidth can be organized and managed as a collection of logical channels.
• These channels can be scheduled to deliver various videos.
Batching
• Make requests wait and then serve together
Video Server
Client
Client
Client
Client
Multicast stream
• Drawbacks: – If the waiting is too long, many requests
may renege
– If the waiting is too short, each multicast can serve only one request
Adaptive Piggybacking
• Video streams are merged by adjusting their playback rates
new arrivals
departures
+5% -5%C B A
• Drawbacks: – The adjustment of playback rate must be small
– Adjusting playback rate needs specialized hardware
Research Challenge
• Each request should be served immediately
• Each multicast should serve a large number
of requests
Motivation Example
videot
patching stream
skew point
regular stream
video player buffer
BA
multicast
Proposed: Patching
A
r
B
p
C
p
D
p
E
r
F
p
G
p
patching window patching window
Multicast group Multicast group
time
Optimal Patching Window
• Server Bandwidth Requirement =
• Candidates of optimal patching window :
D
W
Limitation of Patching Performance
• Patching cost increases as the time gap enlarges
regular streamA t+1t t+20
t0
t0 t+1
patching stream
patching stream
B
C
• Serving B and C takes 2t+1 time units of data
Motivation Example
• Serving B and C takes only t+3 time units of data• About 50% improvement when t is large!
regular streamt+1t t+20
t0
0
long patching stream
short patching stream
t+1 t+2
t+3A
B
C
Observation
• A patching stream is shareable in the next time units if it delivers extra T time units of data
T
regular stream
long patching stream
short patching streamshort patching stream
A
B
C
D
T
2
T / 2
Proposed: Double Patching
r sp sp sp lp sp sp r sp
multicast window
time
patching window patching window
A B C D E F G H I
Multi…
Patch…
• The data delivered during one multicast window– by the regular stream:
– by the long patching streams:
– by the short patching streams:
Performance Optimization
10
20
30
40
50
60
70
5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95
Mea
n Se
rver
Ban
dwid
th R
equi
rem
ent (
Mbi
t/s)
Mean inter-arrival time (seconds)
Standard Patching
Double Patching
Effect of Inter-Arrival Time
Periodic Strategy
• Conventional Periodic Broadcasting [Dan94, Dan96]
– Broadcast period is reduced to
• Research Challenge
– Reduce the broadcast period to ?
Motivation Example
D1 D2 D3 D4
video length
• Design Parameters : ( K = 4, C = 2 )
• Video Segmentation : [ 1, 2, 2, 4 ]
Group 1: D1 D2
Group 2: D3 D4
Motivation Example (con’t)
• Broadcast schedule
Channel 1
Channel 2
Channel 3
Channel 4
D1 D1 D1 D1 D1 D1 D1 D1
D2 D2 D2 D2
D3 D3 D3 D3
D4 D4
Group 1
Group 2
Motivation Example (con’t)
• Clients download segments group by group
Channel 1
Channel 2
Channel 3
Channel 4
a
D1
D2
D3
D4
Group 1
Group 2
Motivation Example (con’t)
• Clients download segments group by group
Channel 1
Channel 2
Channel 3
Channel 4
a
D1
D2
D3
D4
Group 1
Group 2
b
• Continuity within group
• Continuity across group boundary
D2Group 1
D1 D1
D3
Channel 2D2
Channel 3
Motivation Example (con’t)
D2
Download Bandwidth vs. Access Latency
# Channels
1
2
3
4*
Segmentation
[ 1, 1, 1, 1 ]
[ 1, 2, 2, 4 ]
[ 1, 2, 4, 4 ]
[ 1, 2, 4, 8 ]
Latency
Proposed: Client-Centric Approach
• Design Parameters
– K broadcast channels and C download channels
Group
Video Segmentation1 2 .. C
1
2
:K
Cg =
Significance and Impact of CCA
• CCA is the first generalized technique to leverage
receiving bandwidth for more efficient broadcast
• CCA can be modified to support receivers with
different downloading bandwidth [Hua02, Hua03]
What is the limit? • The first segment determines the broadcast period
• How to make this segment as small as possible under
the condition that playback continuity is guaranteed
1. Continuity within group
2. Continuity across group boundary
D2Group 1
D1 D1
D3
Channel 2D2
Channel 3
D2
Question
• What is the maximum size of Si+c?
• Depend on which loader is used to download
L1
Lj
Li+c-1
Segmentation Rule
If Lj is used to download, Si+c can be any size as long as
1) It is a multiple of Sj
2) It is no larger than Sj+Sj+1+Si+c-1
L1
Lj
Li+c-1
Download Schedules (C=2)
S1, 1
S1, 2
S2, 1
S2, 2
Schedule 1
Group 1 Group 2
S2, 1
S2, 2
Group 3
L1
L2
S1, 1
S1, 2
S2, 1
S2, 2
Group 1 Group 2
S2, 1
S2, 2
Group 3
L1
L2
Schedule 2
Broadcast series: 1, 2, 3, 4, 6, 8, 16 ….
Broadcast series: 1, 2, 2, 5, 5, 12, 12, 25, 25 …
Client-Centric Broadcast (CCB)
1. Assuming C-channel receiving capability, we have C! different download schedules
2. For each download schedule, we have one broadcast series
3. Among C! broadcast series, choose the fastest one