Fine Granularity Adaptive
Multi-Receiver Video Streaming
Viktor S. Wold Eide, Frank Eliassen
Jørgen Andreas Michaelsen, and Frank Jensen
viktore@simula.no frank,jorgenam,fnjensen@ifi.uio.no
Simula Research Laboratory and University of Oslo, Norway
MMCN’07
San Jose, CA, USA, 2nd of February 2007
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Viktor S. Wold Eide (Simula, Ifi UiO)
Fine Granularity Adaptive Video Streaming
2nd of February 2007
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Introduction
Outline
Background
Adaptation for Fine Granularity Multi-Receiver Video Streaming
Experimental results
Conclusion and further work
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Viktor S. Wold Eide (Simula, Ifi UiO)
Fine Granularity Adaptive Video Streaming
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Background
Fine Granularity Multi-Receiver Video Streaming
A video coding and streaming scheme we proposed in MMCN’05 article:
multi-receiver streaming
each receiver may customize video quality independently
receiver-driven
scalable:
sender side
network
receiver side
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Background
Fine Granularity Multi-Receiver Video Streaming
Using techniques from MPEG, but designed for scalability
A GOP is divided into fragments that each contribute to a quality
dimension
Each dimension is independent, and each dimension is layered
temporal
luminance quality
chrominance quality
regional
Publish-subscribe based video streaming
receivers customize the quality by subscribing to a certain number of
layers in each dimension
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Demonstration
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Fine Granularity
Adaptive
Multi-Receiver Video Streaming
[simula . research laboratory ]
Viktor S. Wold Eide (Simula, Ifi UiO)
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Challenge
The video quality dimensions are independent
a combinatorial explosion in potential ways to adapt
maintain expressiveness in underlying scheme
consequently, an automatic solution is necessary
Receiver-driven
Automatic Adaptation
Coding and Streaming Scheme
for Fine Granularity MultiReceiver Video Streaming
[simula . research laboratory ]
Viktor S. Wold Eide (Simula, Ifi UiO)
Fine Granularity Adaptive Video Streaming
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Adaptation
The adaptation decision process
User
preferences
Adaptation Decision
Resource
availability
Video quality
subscription
Coding and Streaming Scheme
for Fine Granularity MultiReceiver Video Streaming
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Viktor S. Wold Eide (Simula, Ifi UiO)
Fine Granularity Adaptive Video Streaming
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Resource Availability
The resources considered in this work are
Network bandwidth
CPU utilization
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Viktor S. Wold Eide (Simula, Ifi UiO)
Fine Granularity Adaptive Video Streaming
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Utility Functions
User preferences by means of utility functions
U (t , y , c ) =
N
X
Ui (t , y , c )
(1)
i =0
Ui (t , y , c ) = Yi0 (t , y ) + Ci0 (t , c )
(2)
Yi0 (t , y ) = Yi (y ) + sgn (Yi (y )) Ti (t )
(3)
Ci (t , c ) = Ci (c ) + sgn (Ci (c )) Ti (t )
(4)
0
Wt Wit Kt (t )
(5)
Yi (y ) =
y
Wy Wi Ky (y )
(6)
Ci (c ) =
Wc Wic Kc (c )
(7)
Ti (t ) =
[simula . research laboratory ]
Viktor S. Wold Eide (Simula, Ifi UiO)
Fine Granularity Adaptive Video Streaming
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Experiments
Adaptation scheme evaluated for different user
preferences:
Action — frame rate most important
Detail — frame quality most important
Parallel processing — some superblocks, some part of video signal
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Viktor S. Wold Eide (Simula, Ifi UiO)
Fine Granularity Adaptive Video Streaming
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Experiments: User prefer details, spatial quality
Adaptation to available bandwidth
full
+3
layer
+1
50
base
TL
QY
QC
utility
null
0
utility (%)
75
+2
25
none
200 400 600 800 1000 1200 1400
BW (kbps)
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Experiments: User prefer details, spatial quality
Utility as a function of resource availability
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Experiments: User prefer details, spatial quality
Resource utilization, normalized metric
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Fine Granularity Adaptive Video Streaming
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Summary
An Adaptation Scheme for Fine Granularity
Multi-Receiver Video Streaming
Fine grained adaptation is feasible given radically different user
preferences
Both resource utilization and user utility can be kept at a high level
Adapts to variations in available bandwidth and CPU resources,
roughly over two orders of magnitude
Further work
Quality of adaptation — addressing when and how to adapt
Adjust quality based on visual focus
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Viktor S. Wold Eide (Simula, Ifi UiO)
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