The document discusses research into distributed adaptation frameworks and Scalable Video Coding (SVC) tunneling for edge and in-network media adaptation. It presents the ALICANTE adaptation framework, which introduces new home-box and content-aware network layers. The framework uses an Adaptation Decision-Taking Framework (ADTF) to coordinate adaptation decisions across modules at the content source, network edges, and within the network. It also utilizes SVC tunneling to enable media adaptation at network elements. The research aims to improve network resource utilization while maintaining quality of experience. Initial results show SVC tunneling can reduce bandwidth requirements compared to simulcasting.

1. DISTRIBUTED ADAPTATION
DECISION-TAKING FRAMEWORK AND
SCALABLE VIDEO CODING TUNNELING FOR
EDGE AND IN-NETWORK MEDIA ADAPTATION
Michael Grafl, Christian Timmerer, Markus Waltl,
George Xilouris, Nikolaos Zotos, Daniele Renzi,
Stefano Battista, and Alex Chernilov
TEMU 2012, Heraklion, Greece, July 31, 2012
Michael Grafl et al. Distributed ADTF and SVC Tunneling for Edge and In-Network Media Adaptation 1

2. OUTLINE
 Introduction & Problem Statement
 Research Challenges
 ALICANTE Adaptation Framework
 Adaptation & SVC Tunneling
 Targeted Research Outcomes
 Proposed Integrated Test-Bed
 Scientific Results Achieved So Far
 RC Modes for SVC Tunneling
 Results
 Result Evaluation
 Conclusions
Michael Grafl et al. Distributed ADTF and SVC Tunneling for Edge and In-Network Media Adaptation 2

3. INTRODUCTION & PROBLEM STATEMENT
 Universal Multimedia Access (UMA)
 Evolution of device and network infrastructure
 Heterogeneity of devices, platforms, and networks
 Scalable Video Coding (SVC): bitstream consists of
cumulative layers that refine the video (resolution,
framerate, bitrate)
 SVC tunneling approach featuring edge and in-network
media adaptation (for streaming)
 Content-Aware Networking (CAN) as
evolutionary approach towards the Future Internet
Michael Grafl et al. Distributed ADTF and SVC Tunneling for Edge and In-Network Media Adaptation 3

4. RESEARCH CHALLENGES
 Distributed adaptation decision-taking framework
 Where to adapt? – at source, in-network, receiver, and
combinations thereof
 When to adapt? – at request and during delivery
 How often to adapt? – too often (risk: flickering), too seldom
(risk: stalling)
 How to adapt? – optimization towards resolution, framerate,
SNR (bitrate), accessibility, etc.; (too) many possibilities
 Efficient, scalable SVC tunneling and signaling thereof
 Low (end-to-end) delay, minimum quality degradation, scalability
(# parallel sessions)
 Impact on the Quality of Service/Experience (QoS/QoE)
 Trade-off (for certain use cases and applications); QoS  QoE
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5. ALICANTE ADAPTATION FRAMEWORK
 FP7 ICT project
 "Media Ecosystem Deployment through Ubiquitous
Content-Aware Network Environments"
 Goal: New Home-Box layer and CAN layer with
cross-layer adaptation enabling cooperation between
providers, operators, and end-users
 2 new virtual layers
 Home-Box (HB) Layer: enhanced home gateways
 CAN Layer: content-aware adaptation of SVC at
Media-Aware Network Elements (MANEs)
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6. ALICANTE ADAPTATION FRAMEWORK
End-to-End Multimedia Communication (MPEG-2, MPEG-4, AVC, SVC, ...)
Context-
Aware
Adaptation HB HB
Home-Box Layer
HB
HB
HB
SVC (Layered-Multicast) Tunnel
CAN ... CAN Dynamic,
Network-Aware
MANE MANE MANE MANE Adaptation
Autonomous
System
... Autonomous
System
Michael Grafl et al. Distributed ADTF and SVC Tunneling for Edge and In-Network Media Adaptation 6

7. ADAPTATION & SVC TUNNELING
 Adaptation Decision-Taking Framework (ADTF) coordinating
local adaptation decisions of modules at
 the content source;
 the border to the user (Home-Box); and
 within the network at MANEs
 SVC (layered-multicast) tunnel
 Adaptation of scalable media resource at MANE
 At the border to the user (Home-Box), adaptation modules are
deployed enabling device-independent access
 Key Innovations
 Better network resource utilization & maintaining
a satisfactory Quality of Experience
 Adaptation decision aggregation and propagation
 Distributed coordination with CAN layer for optimal adaptation &
improved bandwidth usage
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8. TARGETED RESEARCH OUTCOMES
 Guidelines for scalable media encoding/transcoding parameters
(with SVC as example)
 Guidelines for distributed adaptation decision-taking framework
 Enhancement of
 decision-taking algorithm by exploiting active and passive monitoring
 SVC adaptation based on network load/conditions and QoS constraints
using a content-aware approach
 Assessment of the performance and scalability (e.g., number of
flows, flow traffic profile)
 computing resources utilized (e.g., CPU and memory)
 network related metrics (e.g., processing delay per flow, maximum achieved
bandwidth)
 Mappings of network and device monitoring parameters
 Enable prediction of QoE; validation through subjective quality assessments
 Holistic approach for in-network adaptation applying different
adaptation policies per content-aware virtual network
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9. PROPOSED INTEGRATED TEST-BED
Michael Grafl et al. Distributed ADTF and SVC Tunneling for Edge and In-Network Media Adaptation 9

10. SCIENTIFIC RESULTS ACHIEVED SO FAR
 Achieved results
 Quality impact of SVC tunneling using MPEG-2 as
starting point: baseline for further research [3]
 Initial performance evaluations of SVC streaming and
real-time in-network adaptation [4]
 End-to-end QoS control including a model for
QoS-QoE mapping [5]
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11. RC MODES FOR SVC TUNNELING
 Comparing rate control (RC) modes for SVC tunneling
 Extended previous tests [3] to compare SVC tunneling for
• Variable bitrate (VBR) constant quantization parameter (QP)
• Constant bitrate (CBR)
• Different codecs: bSoft, MainConcept
• SVC config: 4 medium-grained scalability (MGS) layers
 Procedure:
• Pixel-domain transcoding (PDT) from MPEG-2 to SVC
• Transcode resulting bitstream back from SVC to MPEG-2
• Measured Bjontegaard Delta (BD) Y-PSNR
• Compared required bandwidths to MPEG-2 simulcast
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12. RESULTS (1)
MainConcept MainConcept
bSoft (VBR)
(VBR) (CBR)
Sequence BD- BD- BD- BD- BD- BD-
PSNR bitrate PSNR bitrate PSNR bitrate
[dB] [%] [dB] [%] [dB] [%]
foreman -2.08 50.3 -2.03 53.7 -2.40 61.6
container -1.57 38.2 -1.99 51.0 -2.91 66.9
hall_monitor -0.75 22.6 -1.40 54.1 -1.82 73.6
stefan -2.59 41.0 -2.09 32.1 -2.88 53.4
Average -1.74 38.04 -1.88 47.7 -2.50 63.9
Table 1: Bjontegaard Delta for SVC tunneling
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13. RESULTS (2)
MainConcept MainConcept
Target Quality bSoft (VBR)
(VBR) (CBR)
SVC SVC MPEG-2 SVC MPEG-2 SVC MPEG-2
VBR CBR
encoding tunnel simulcast tunnel simulcast tunnel simulcast
[QP] [Mbps]
config [kbps] [kbps] [kbps] [kbps] [kbps] [kbps]
Q1 16 3 5333 3041 3694 3454 3286 4721
Q2 20 2 3446 2025 2418 2082 2242 3191
Q3 24 1.5 2201 1452 1650 1277 1687 2093
Q4 28 1 1438 1102 1132 900 1109 1287
Average 3105 1905 2224 1928 2081 2823
Table 2: Comparison of required bandwidths for
SVC tunneling vs. MPEG-2 simulcast
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14. RESULT EVALUATION
 Less quality impact for VBR mode
 CBR mode: SVC tunneling more bandwidth
efficient than MPEG-2 simulcast (~26% reduction)
 Bandwidth efficiency of SVC tunneling depends
on number and configuration of SVC layers
(mainly on quality of Base Layer)
 Other scenarios: VBR mode SVC tunneling
favorable to MPEG-2 simulcast if only server-side
transcoding needed (i.e., client supports SVC)
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15. CONCLUSIONS
 Research challenges and key innovations for edge
and in-network adaptation
 SVC tunneling
 Distributed Adaptation
 Performance evaluations of SVC streaming
 End-to-end QoS control & QoS-QoE mapping approach
 CBR and VBR mode for SVC tunneling compared
 Integrated test-bed proposed
 Future work: HD content; subjective tests; integrate
QoS-QoE mapping; multi-video rate allocation
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16. SELECTED LITERATURE
[1] F. Pereira and I. Burnett, "Universal multimedia experiences for
tomorrow," IEEE Signal Processing Magazine, vol.20, no.2, Mar.
2003.
[2] European Commission, "ALICANTE, Annex I – Description of Work,"
FP7-ICT-2009-4, Grant agreement no. 248652, 2009.
[3] M. Grafl, C. Timmerer, and H. Hellwagner, "Quality Impact of
Scalable Video Coding Tunneling for Media-Aware Content
Delivery," Proc. ICME’11, Barcelona, Spain, July 2011.
[4] N. Zotos et al., "Performance evaluation of H264/SVC streaming
system featuring real-time in-network adaptation," Proc. IWQoS’11,
San Jose, California, June 2011.
[5] B. Shao et al., "An Adaptive System for Real-Time Scalable Video
Streaming with End-to-End QoS Control," Proc. WIAMIS’10,
Desenzano Del Garda, Italy, Apr. 2010.
[6] G. Bjontegaard, "Improvements of the BD-PSNR model," ITU-T
SG16/Q6, 2008.
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17. THANK YOU FOR YOUR ATTENTION!
Questions?
http://ict-alicante.eu/
http://itec.uni-klu.ac.at/~mgrafl
Michael Grafl et al. Distributed ADTF and SVC Tunneling for Edge and In-Network Media Adaptation 17