1. F.LIVE
Shannon Chen (cchen116@Illinois.edu),
Zhenhuan Gao, and Klara Nahrstedt
University of Illinois at Urbana-Champaign
Towards Interactive Live Broadcast
Free-viewpoint TV Experience
This material is based in part upon work supported by
the National Science Foundation (NeTS-0520182)
9. EXISTING FTV
DELIVERY FRAMEWORKS
• Type-1: View chosen by content provider
• EyeVision System [Kanade ‘01] used in Super Bowl XXXV
Editing DecodeAggregateCapture
Capture
Capture
Encode
Director
Viewpoint decision
Audience
• Interactive
• Live
10. EXISTING FTV
DELIVERY FRAMEWORKS
• Type-2: Aggregated stream bundle
• Nagoya System [Tanimoto ‘12]
• Interactive
• Live
DecodeAggregateCapture
Audience
Capture
Viewpoint decision
Capture
Encode
31. FOREST PLANNING
CHALLENGES
• If we have all the subscription information at the beginning
of forest planning , an optimal planning is not a hard
problem
• However, subscriptions are dynamic
• P2P churn
• View change “Forest adaptation”
Initial forest
construction
Cam
B
Cam
A
Cam
C
32. FOREST PLANNING
CHALLENGES
• If we have all the subscription information at the beginning
of forest planning , an optimal planning is not a hard
problem
• However, subscriptions are dynamic
• P2P churn
• View change “Forest adaptation”
Initial forest
construction
Cam
B
Cam
A
Cam
C
Audience
join
Bandwidth?
Freshness?
33. FOREST PLANNING
CHALLENGES
• If we have all the subscription information at the beginning
of forest planning , an optimal planning is not a hard
problem
• However, subscriptions are dynamic
• P2P churn
• View change “Forest adaptation”
Initial forest
construction
Cam
B
Cam
A
Cam
C
Audience
leave
34. FOREST PLANNING
CHALLENGES
• If we have all the subscription information at the beginning
of forest planning , an optimal planning is not a hard
problem
• However, subscriptions are dynamic
• P2P churn
• View change “Forest adaptation”
Initial forest
construction
Cam
B
Cam
A
Cam
C
Audience
leave
3 4
35. FOREST PLANNING
CHALLENGES
• If we have all the subscription information at the beginning
of forest planning , an optimal planning is not a hard
problem
• However, subscriptions are dynamic
• P2P churn
• View change “Forest adaptation”
Initial forest
construction
Cam
B
Cam
A
Cam
C
View
change
36. FOREST PLANNING
CHALLENGES
• If we have all the subscription information at the beginning
of forest planning , an optimal planning is not a hard
problem
• However, subscriptions are dynamic
• P2P churn
• View change “Forest adaptation”
Initial forest
construction
Cam
B
Cam
A
Cam
C
View
change
3 3
37. FOREST PLANNING
CHALLENGES
• If we have all the subscription information at the beginning
of forest planning , an optimal planning is not a hard
problem
• However, subscriptions are dynamic
• P2P churn
• View change
We do not aim for optimization (no tearing down trees)
Heuristics that deal with new batch of subscription requests
(audience join/leave/view change)
Details on initial construction and adaptation algorithms (w/
pseudocodes) can be found in the paper
[Shannon Chen et al. INFOCOM’16]
“Forest adaptation”
Initial forest
construction
38. EVALUATION
• Simulation settings
• Single TV-studio performer site
• Network capability of audience sites: [Netmap]
• In/out-bound bandwidth, site-to-site propagation delay
• Simulate new subscription requests when there are 0 to
100,000 audiences in the session
High-resolution
cameras
Moderate 100-camera array
# of cameras 16 30 100
Camera framerate 30 FPS 30 FPS 30 FPS
Camera bitrate 12 Mbps (HDTV) 6 Mbps 2 Mbps (SDTV)
40. SYNCHRONIZATION DELAY
• Unstable at first: not many candidates for newly
joined/rejoined audience to find a group of sources with
similar propagation delays
• Delay for handling new coming subscriptions during
application session is in 100ms-scale in stable state
0
1000
0 500 1000Audience group size
High-Res Setting
0
1000
0 500 1000
Audience group size
Moderate Setting
0
1000
0 5000 10000Audience group size
100-Camera Setting
Syncdelay(ms)
Syncdelay(ms)
Syncdelay(ms)
41. PRODUCER BANDWIDTH
CONSUMPTION
• P2P sharing restricts the growth of bandwidth consumption
• Outbound bandwidth requirement is well-manageable by
Gbps infrastructure
0
250
500
0 500 1000
Totalproducer…
Audience group size
High Bitrate Setting
0
250
500
0 500 1000
Totalproducer…
Audience group size
Moderate Setting
0
250
500
0 2500 5000
Totalproducer…
Audience group size
100-Camera Setting
42. CONTENT FRESHNESS
• > 50% audience have higher-than-average elapses
• But the tree structure makes the elapse grows sub-linearly
• Max elapse < 4.5 seconds
(compare: CBS TV network’s time elapse is 5 sec)
43. COMPARE TO OTHER FTV
FRAMEWORKS
Editing DecodeAggregateCap
Cap
Cap
Encode
Audience
DecodeAggregateCap
Audience
Cap
Viewpoint decision
Cap
Encode
Type-1: customized content
Type-2: aggregated content
Viewpoint decision
MVC
44. COMPARE TO OTHER FTV
FRAMEWORKS
0
50
100
150
Bandwidthconsumptionof
audiencesite(Mpbs)
High-Res
Moderate
100-Cameras
Type-1 (dash)
View-based (solid)
High-Res
Moderate
100-Cameras
Type-2 (stripe); View-based (solid)
Producer site bandwidth
consumption
Audience site bandwidth
consumption
45. CONCLUSION
• We propose a new FTV content delivery framework which
aims at co-existence of three desired features
• Interactive
• Live
• Broadcasting
• Result of large-scale simulation shows the proposed F.Live
framework with view-based delivery chain achieves
• Interactive response time in 100ms-scale
• Acceptable content freshness by TV industry standard
• Feasible bandwidth consumption while sustaining 1000-scale
audience group
