MMCN’05
Exploiting Content-Based Networking for Fine
Granularity Multi-Receiver Video Streaming
Viktor S. Wold Eidea,b Frank Eliassena Jørgen Andreas Michaelsenb
a
Simula Research Laboratory , Lysaker, Norway
{viktore,frank}@simula.no
b
University of Oslo , Norway
{viktore,jorgenam}@ifi.uio.no
http://www.ifi.uio.no/˜dmj/
http://www.simula.no:8888/QuA/
Twelfth Annual Multimedia Computing and Networking (MMCN ’05)
January 19-20, 2005, San Jose, California, USA
Part of Electronic Imaging Symposium
Simula Research Laboratory and University of Oslo, Norway
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MMCN’05
Motivated by observing that multi-receiver
video streaming still represents a challenge
Unicast solutions, only big players can provide live video to many receivers
Network layer multicast is still not ubiquitous
However, if multicast was ubiquitous, heterogeneity would still be a challenge
Simula Research Laboratory and University of Oslo, Norway
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MMCN’05
The goal is to allow each video receiver to
customize the video quality independently
A video receiver may then balance resource usage against cost and preferences
The number of interested video receivers should not affect the video server
The network should be relatively unaffected by the number of video receivers
Simula Research Laboratory and University of Oslo, Norway
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MMCN’05
Our suggestion is to break the traditional
assumption of destination (group) addressing
Traditionally, a video server adds an explicit destination (group) address to each
outgoing packet. The address then determines the forwarding and delivery
In contrast, our approach is to use implicit addressing, where the contents of each
packet and the receiver interests determine the delivery
Hence, we suggest exploiting content-based networking for fine granularity
multi-receiver video streaming
Simula Research Laboratory and University of Oslo, Norway
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The rest of the talk presents the bridging of
content-based networking and video streaming
The following will be discussed:
• Content-based networking
• An encoding scheme for fine granularity video streaming
• Video streaming demonstration
• Empirical results
• Conclusion and further work
Simula Research Laboratory and University of Oslo, Norway
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Content-based networking have some
distinguishing characteristics
Messages, called notifications, are forwarded based on content, and not on an
explicit address. The notifications:
• consist of attribute/value-pairs: [ sid=10 col=1 row=2 blob=... ]
• are delivered to interested clients based on subscriptions: [ sid=10 col>=2 ]
• are pruned upstream and replicated downstream
• may be transported over different technologies, such as TCP, UDP, and IP
multicast
Simula Research Laboratory and University of Oslo, Norway
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Content-based networking example, including
a single video server and some video receivers
Content−Based Network
Publish
Video server
Video client(s)
Network node
Intra domain
Subscribe
Notify
Subscribe
Subscribe
Notify
Notify
Simula Research Laboratory and University of Oslo, Norway
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MMCN’05
A fine granularity coding scheme allows each
video receiver to customize the video stream
Well known video coding techniques have been adopted to provide each video
receiver with independent selectivity along multiple video quality dimensions:
• region of interest (superblocks)
• signal to noise ratio (DCT coefficients quantized and viewed as bit-planes)
• colors (separating luminance and chrominance information)
• temporal resolution (layered structure)
Simula Research Laboratory and University of Oslo, Norway
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MMCN’05
Region of interest selectivity is supported by
means of so-called superblocks
Region of interest selectivity
row
col
0
1
0
1
n
superblock
macroblock
block
Y0 Y1
Y2 Y3
U
V
m
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SNR selectivity is supported by means of
bit-plane coding
Signal to noise ratio selectivity
bit
ql
sign
10 MSB
... MSB − 1
0
0 Advertise
1
2
3
0 1 2
62 63
...
DCT values of 8x8 block
Simula Research Laboratory and University of Oslo, Norway
2
1
0
LSB
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Each frame is broken up, encoded, and
published in a number of notifications
Video fragments
...
Publish
Notifications
sid=1 tl=2 ql=0 f=0 row=2 col=3 blob=...
sid=1 tl=2 ql=3 f=1 row=1 col=3 blob=...
Video server
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The content-based network forwards the
notifications based on subscriptions
Notify
CB
Subscriptions
Map
Publish
CB
IP
Subscriptions
Map
IP
Notify
CB
234.0.80.3
234.0.80.2
Subscriptions
Map
IP
Network
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Each notification is delivered to all receivers
having subscriptions matching the notification
Notify
First
Video Client
Notify
Second
Video Client
Video Receivers
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Video streaming demonstration
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A layer of indirection is provided between the
video server and the video receivers
CB
Notify
Subscriptions
Map
CB
IP
Subscriptions
...
Publish
Map
CB
Video fragments
IP
sid=1 tl=2 ql=0 f=0 row=0 col=3 data=[...]
Notifications
234.0.80.3
234.0.80.2
Notify
Subscriptions
Map
sid=1 tl=2 ql=3 f=1 row=1 col=1 data=[...]
IP
Video server
First
Video Client
Network
Simula Research Laboratory and University of Oslo, Norway
Second
Video Client
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Experimental results indicate that network and
CPU usage scales well
By reducing the video quality in one or more dimensions, the resource
consumption is reduced accordingly
Hence, a video receiver may independently tradeoff resource usage against video
quality in the different dimensions
Simula Research Laboratory and University of Oslo, Norway
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When receiving only some regions, the CPU
and network consumption drops accordingly†
News CIF Y, 30 frames pr. second
col row % CPU bpp intra bpp diff kbps
0
0
9.50
0.70
0.16 116
0
1 11.82
1.20
0.29 208
0
2
5.79
1.52
0.03 114
1
0
7.08
0.88
0.20 147
1
1 11.99
1.34
0.40 268
1
2
8.17
1.44
0.12 149
Sum
54.35
1.18
0.20 1002
All
55.37
1.18
0.20 1003
†
Results for the other dimensions are in the paper
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Content-based networking seems promising
for fine grained multi-receiver video streaming
The contribution of this work is bridging content-based networking with
techniques from the fields of video compression and streaming
Each video receiver may independently customize region of interest, colors,
SNR, and temporal resolution, while efficiency (network and CPU) is maintained
The software, written in Java, is available as open source from the project web
pages
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There are many opportunities and challenges
with respect to further work
We are currently working on:
• motion compensation
• adaptation, e.g., by automatically changing subscriptions
• spatial scalability
We are also considering:
• closer integration with MPEG-4
• storage formats suited for non real-time usage, e.g., video on demand
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