SlideShare a Scribd company logo
1 of 15
MPEG-21-based Cross-Layer Optimization Techniques for enabling Quality of Experience Christian Timmerer Klagenfurt University (UNIKLU)  Faculty of Technical Sciences (TEWI) Department of Information Technology (ITEC)  Multimedia Communication (MMC) http://research.timmerer.com  http://blog.timmerer.com  mailto:christian.timmerer@itec.uni-klu.ac.at Acknowledgments: DANAE, ENTHRONE, P2P-Next, ALICANTE projects funded by EC, SCALIPTV/SCALNET funded by FFG, ASSSV funded by FWF and, in particular Michael Eberhard, Ingo Kofler, Robert Kuschnig, Michael Ransburg, Michael Sablatschan, Hermann Hellwagner
Outline Background / Introduction Cross-layer designs & optimizations MPEG-21 Digital Item Adaptation How to exploit MPEG-21 for XL optimizations? Cross-Layer Model (XLM) Instantiation of the XLM by utilizing MPEG-21 metadata Cross-Layer Adaptation Decision-Taking Engine (XL-ADTE) Conclusions 2010/01/20 2 Christian Timmerer, Klagenfurt University, Austria
Background / Introduction Cross-layer designs Aim: increase QoS/QoEby performing coordinated actions across the network layers => violating the protocol hierarchy and isolation model  Approaches: bottom-up or a top-down or jointly optimizing parameters at the different layers Common property: compromising interoperability in favor of performance Increasing the interoperability of cross-layer designs by adopting an open standard – MPEG-21 Digital Item Adaptation – for describing the functional dependencies across network layers 2010/01/20 Christian Timmerer, Klagenfurt University, Austria 3
Digital Item Adaptation DIA := syntax and semantics of tools that assist in the adaptation of Digital Items Goals: Satisfy transmission, storage andconsumption constraints as well asQuality of Service (QoS) management Enable transparent access to (distributed)advanced multimedia content by shieldingusers from network and terminal installationissues Codec Format-independent mechanisms that provide support for Digital Item Adaptation in terms of: Resource adaptation Description adaptation Quality of Service management The adaptation engines themselves are non-normative tools 2010/01/20 Christian Timmerer, Klagenfurt University, Austria 4
2010/01/20 Christian Timmerer, Klagenfurt University, Austria Usage Environment Description (UED) Terminal Capabilities ,[object Object]
 Device Properties
 Input-Output CharacteristicsUser Characteristics ,[object Object]
 Usage Preference & History
 Presentation Preferences
 Accessibility
 Locationfundamental inputto any adaptation engine Natural Environment Characteristics ,[object Object]
 Audio-VisualNetwork Characteristics ,[object Object]
 Conditions5 Context-related metadata describes the usage environment in terms of terminal capabilities; network characteristics; user characteristics; natural environment characteristics; e.g., codec capabilities = mp2, ML@MP; available bandwidth=1500kbps; visually impaired; high-level ambient noise;
AdaptationQoS and Universal Constraints Description Content-related metadata – AdaptationQoS– describes the relationship between constraints; feasible adaptation operations satisfying these constraints; associated utilities (qualities); e.g., available bandwidth is 384kbps, terminal display is CIF; reduce bit-rate; quality at QCIF/30fps/QP=10 versus CIF/10fps/QP=15e.g., bit-rate = 256kbps, frame-rate=30fps, resolution=CIF, etc. Universal Constraints Description (UCD): mathematical approach based on an optimization problem find values for the variables representing adaptation parameters that do not violate the limitation constraints (feasibility) and maximize the optimization constraint(optimality, objective function) 2010/01/20 Christian Timmerer, Klagenfurt University, Austria 6
How to exploit MPEG-21 for XL optimizations? Three-step approach Cross-Layer Model (XLM): describing the relationship between QoS metrics at different levels  No specific notation (e.g., graphical) For example:  Instantiation of the XLM by utilizing MPEG-21 metadata AdaptationQoS (AQoS): describe the relationship between constraints, feasible adaptation operations satisfying these constraints, and associated utilities (qualities) Usage Environment Description (UED): context information (network conditions, terminal capabilities, user preferences, etc.) Universal Constraints Description (UCD): limitation and optimization constraints  Cross-Layer Adaptation Decision-Taking Engine (XL-ADTE) Software module solving an optimization problem adopting any algorithm 2010/01/20 Christian Timmerer, Klagenfurt University, Austria 7

More Related Content

What's hot

DOWNLOAD
DOWNLOADDOWNLOAD
DOWNLOAD
butest
 

What's hot (12)

IP-optical convergence: a complete solution
IP-optical convergence: a complete solutionIP-optical convergence: a complete solution
IP-optical convergence: a complete solution
 
Future Mobile Telecommunication Networks Using Cloud Technologies
Future Mobile Telecommunication Networks Using Cloud TechnologiesFuture Mobile Telecommunication Networks Using Cloud Technologies
Future Mobile Telecommunication Networks Using Cloud Technologies
 
A DDRESSING T HE M ULTICHANNEL S ELECTION , S CHEDULING A ND C OORDINATION...
A DDRESSING  T HE  M ULTICHANNEL S ELECTION , S CHEDULING  A ND C OORDINATION...A DDRESSING  T HE  M ULTICHANNEL S ELECTION , S CHEDULING  A ND C OORDINATION...
A DDRESSING T HE M ULTICHANNEL S ELECTION , S CHEDULING A ND C OORDINATION...
 
Designing an lte channel for data transmission
Designing an lte channel for data transmissionDesigning an lte channel for data transmission
Designing an lte channel for data transmission
 
A Framework for Adaptive Delivery of Omnidirectional Video
A Framework for Adaptive Delivery of Omnidirectional VideoA Framework for Adaptive Delivery of Omnidirectional Video
A Framework for Adaptive Delivery of Omnidirectional Video
 
ADAPTIVE
ADAPTIVEADAPTIVE
ADAPTIVE
 
Digital Image Watermarking using DWT and SVD
Digital Image Watermarking using DWT and SVDDigital Image Watermarking using DWT and SVD
Digital Image Watermarking using DWT and SVD
 
Digital image protection using adaptive watermarking techniques
Digital image protection using adaptive watermarking techniquesDigital image protection using adaptive watermarking techniques
Digital image protection using adaptive watermarking techniques
 
T2826552
T2826552T2826552
T2826552
 
Vital Alert Announces MAGI-SIM = VLF Propagation Modeling Tool
Vital Alert Announces MAGI-SIM  = VLF Propagation Modeling ToolVital Alert Announces MAGI-SIM  = VLF Propagation Modeling Tool
Vital Alert Announces MAGI-SIM = VLF Propagation Modeling Tool
 
Advance Digital Video Watermarking based on DWT-PCA for Copyright protection
Advance Digital Video Watermarking based on DWT-PCA for Copyright protectionAdvance Digital Video Watermarking based on DWT-PCA for Copyright protection
Advance Digital Video Watermarking based on DWT-PCA for Copyright protection
 
DOWNLOAD
DOWNLOADDOWNLOAD
DOWNLOAD
 

Viewers also liked (11)

H.264 Encoder Nal Packet Formation By Sbs
H.264 Encoder Nal Packet Formation By SbsH.264 Encoder Nal Packet Formation By Sbs
H.264 Encoder Nal Packet Formation By Sbs
 
Video streaming on e-lab
Video streaming on e-labVideo streaming on e-lab
Video streaming on e-lab
 
Vertical handoff and TCP performance optimizations using cross layer approach
Vertical handoff and TCP performance optimizations using cross layer approachVertical handoff and TCP performance optimizations using cross layer approach
Vertical handoff and TCP performance optimizations using cross layer approach
 
Boletín de Novedades literatura Junio
Boletín de Novedades literatura JunioBoletín de Novedades literatura Junio
Boletín de Novedades literatura Junio
 
The MPEG-21 Multimedia Framework
The MPEG-21 Multimedia FrameworkThe MPEG-21 Multimedia Framework
The MPEG-21 Multimedia Framework
 
data - driven journalism 2
data - driven journalism 2data - driven journalism 2
data - driven journalism 2
 
MPEG-21 Digital Items in Research and Practice
MPEG-21 Digital Items in Research and PracticeMPEG-21 Digital Items in Research and Practice
MPEG-21 Digital Items in Research and Practice
 
Mpeg 7
Mpeg 7Mpeg 7
Mpeg 7
 
Qo s provisioning for scalable video streaming over ad hoc networks using cro...
Qo s provisioning for scalable video streaming over ad hoc networks using cro...Qo s provisioning for scalable video streaming over ad hoc networks using cro...
Qo s provisioning for scalable video streaming over ad hoc networks using cro...
 
Mpeg7
Mpeg7Mpeg7
Mpeg7
 
H.264 nal and RTP
H.264 nal and RTPH.264 nal and RTP
H.264 nal and RTP
 

Similar to MPEG-21-based Cross-Layer Optimization Techniques for enabling Quality of Experience

HTTP Adaptive Streaming – Quo Vadis? (2023)
HTTP Adaptive Streaming – Quo Vadis? (2023)HTTP Adaptive Streaming – Quo Vadis? (2023)
HTTP Adaptive Streaming – Quo Vadis? (2023)
Alpen-Adria-Universität
 
Overview of Selected Current MPEG Activities
Overview of Selected Current MPEG ActivitiesOverview of Selected Current MPEG Activities
Overview of Selected Current MPEG Activities
Alpen-Adria-Universität
 
Overview of Selected Current MPEG Activities
Overview of Selected Current MPEG ActivitiesOverview of Selected Current MPEG Activities
Overview of Selected Current MPEG Activities
Alpen-Adria-Universität
 
Resume-LIN-en-2014
Resume-LIN-en-2014Resume-LIN-en-2014
Resume-LIN-en-2014
lin xianjin
 
Resume-LIN-en-2014
Resume-LIN-en-2014Resume-LIN-en-2014
Resume-LIN-en-2014
lin xianjin
 
“Introduction to the TVM Open Source Deep Learning Compiler Stack,” a Present...
“Introduction to the TVM Open Source Deep Learning Compiler Stack,” a Present...“Introduction to the TVM Open Source Deep Learning Compiler Stack,” a Present...
“Introduction to the TVM Open Source Deep Learning Compiler Stack,” a Present...
Edge AI and Vision Alliance
 
Standardising the compressed representation of neural networks
Standardising the compressed representation of neural networksStandardising the compressed representation of neural networks
Standardising the compressed representation of neural networks
Förderverein Technische Fakultät
 

Similar to MPEG-21-based Cross-Layer Optimization Techniques for enabling Quality of Experience (20)

UDT
UDTUDT
UDT
 
On MPEG Modern Transport over Network
On MPEG Modern Transport over NetworkOn MPEG Modern Transport over Network
On MPEG Modern Transport over Network
 
UDT
UDTUDT
UDT
 
HTTP Adaptive Streaming – Quo Vadis? (2023)
HTTP Adaptive Streaming – Quo Vadis? (2023)HTTP Adaptive Streaming – Quo Vadis? (2023)
HTTP Adaptive Streaming – Quo Vadis? (2023)
 
Overview of Selected Current MPEG Activities
Overview of Selected Current MPEG ActivitiesOverview of Selected Current MPEG Activities
Overview of Selected Current MPEG Activities
 
Overview of Selected Current MPEG Activities
Overview of Selected Current MPEG ActivitiesOverview of Selected Current MPEG Activities
Overview of Selected Current MPEG Activities
 
Design and implementation of DADCT
Design and implementation of DADCTDesign and implementation of DADCT
Design and implementation of DADCT
 
Quality of Service for Video Streaming using EDCA in MANET
Quality of Service for Video Streaming using EDCA in MANETQuality of Service for Video Streaming using EDCA in MANET
Quality of Service for Video Streaming using EDCA in MANET
 
Resume-LIN-en-2014
Resume-LIN-en-2014Resume-LIN-en-2014
Resume-LIN-en-2014
 
Resume-LIN-en-2014
Resume-LIN-en-2014Resume-LIN-en-2014
Resume-LIN-en-2014
 
2 han
2 han2 han
2 han
 
“Introduction to the TVM Open Source Deep Learning Compiler Stack,” a Present...
“Introduction to the TVM Open Source Deep Learning Compiler Stack,” a Present...“Introduction to the TVM Open Source Deep Learning Compiler Stack,” a Present...
“Introduction to the TVM Open Source Deep Learning Compiler Stack,” a Present...
 
6044847.ppt
6044847.ppt6044847.ppt
6044847.ppt
 
Qcom XR Workshop Sept 2020
Qcom XR Workshop Sept 2020Qcom XR Workshop Sept 2020
Qcom XR Workshop Sept 2020
 
Accelerating Media Business Developments, MPEG-M: MPEG Extensible Middleware
Accelerating Media Business Developments, MPEG-M: MPEG Extensible MiddlewareAccelerating Media Business Developments, MPEG-M: MPEG Extensible Middleware
Accelerating Media Business Developments, MPEG-M: MPEG Extensible Middleware
 
New coding techniques, standardisation, and quality metrics
New coding techniques, standardisation, and quality metricsNew coding techniques, standardisation, and quality metrics
New coding techniques, standardisation, and quality metrics
 
The MPEG-21 Multimedia Framework for Integrated Management of Environments en...
The MPEG-21 Multimedia Framework for Integrated Management of Environments en...The MPEG-21 Multimedia Framework for Integrated Management of Environments en...
The MPEG-21 Multimedia Framework for Integrated Management of Environments en...
 
Standardising the compressed representation of neural networks
Standardising the compressed representation of neural networksStandardising the compressed representation of neural networks
Standardising the compressed representation of neural networks
 
HTTP Streaming of MPEG Media
HTTP Streaming of MPEG MediaHTTP Streaming of MPEG Media
HTTP Streaming of MPEG Media
 
4g lte matlab
4g lte matlab4g lte matlab
4g lte matlab
 

More from Alpen-Adria-Universität

Optimal Quality and Efficiency in Adaptive Live Streaming with JND-Aware Low ...
Optimal Quality and Efficiency in Adaptive Live Streaming with JND-Aware Low ...Optimal Quality and Efficiency in Adaptive Live Streaming with JND-Aware Low ...
Optimal Quality and Efficiency in Adaptive Live Streaming with JND-Aware Low ...
Alpen-Adria-Universität
 
Content-adaptive Video Coding for HTTP Adaptive Streaming
Content-adaptive Video Coding for HTTP Adaptive StreamingContent-adaptive Video Coding for HTTP Adaptive Streaming
Content-adaptive Video Coding for HTTP Adaptive Streaming
Alpen-Adria-Universität
 
Evaluation of Quality of Experience of ABR Schemes in Gaming Stream
Evaluation of Quality of Experience of ABR Schemes in Gaming StreamEvaluation of Quality of Experience of ABR Schemes in Gaming Stream
Evaluation of Quality of Experience of ABR Schemes in Gaming Stream
Alpen-Adria-Universität
 
Policy-Driven Dynamic HTTP Adaptive Streaming Player Environment
Policy-Driven Dynamic HTTP Adaptive Streaming Player EnvironmentPolicy-Driven Dynamic HTTP Adaptive Streaming Player Environment
Policy-Driven Dynamic HTTP Adaptive Streaming Player Environment
Alpen-Adria-Universität
 
Energy Consumption in Video Streaming: Components, Measurements, and Strategies
Energy Consumption in Video Streaming: Components, Measurements, and StrategiesEnergy Consumption in Video Streaming: Components, Measurements, and Strategies
Energy Consumption in Video Streaming: Components, Measurements, and Strategies
Alpen-Adria-Universität
 
Exploring the Energy Consumption of Video Streaming: Components, Challenges, ...
Exploring the Energy Consumption of Video Streaming: Components, Challenges, ...Exploring the Energy Consumption of Video Streaming: Components, Challenges, ...
Exploring the Energy Consumption of Video Streaming: Components, Challenges, ...
Alpen-Adria-Universität
 
SARENA: SFC-Enabled Architecture for Adaptive Video Streaming Applications
SARENA: SFC-Enabled Architecture for Adaptive Video Streaming ApplicationsSARENA: SFC-Enabled Architecture for Adaptive Video Streaming Applications
SARENA: SFC-Enabled Architecture for Adaptive Video Streaming Applications
Alpen-Adria-Universität
 

More from Alpen-Adria-Universität (20)

VEED: Video Encoding Energy and CO2 Emissions Dataset for AWS EC2 instances
VEED: Video Encoding Energy and CO2 Emissions Dataset for AWS EC2 instancesVEED: Video Encoding Energy and CO2 Emissions Dataset for AWS EC2 instances
VEED: Video Encoding Energy and CO2 Emissions Dataset for AWS EC2 instances
 
GREEM: An Open-Source Energy Measurement Tool for Video Processing
GREEM: An Open-Source Energy Measurement Tool for Video ProcessingGREEM: An Open-Source Energy Measurement Tool for Video Processing
GREEM: An Open-Source Energy Measurement Tool for Video Processing
 
Optimal Quality and Efficiency in Adaptive Live Streaming with JND-Aware Low ...
Optimal Quality and Efficiency in Adaptive Live Streaming with JND-Aware Low ...Optimal Quality and Efficiency in Adaptive Live Streaming with JND-Aware Low ...
Optimal Quality and Efficiency in Adaptive Live Streaming with JND-Aware Low ...
 
VEEP: Video Encoding Energy and CO₂ Emission Prediction
VEEP: Video Encoding Energy and CO₂ Emission PredictionVEEP: Video Encoding Energy and CO₂ Emission Prediction
VEEP: Video Encoding Energy and CO₂ Emission Prediction
 
Content-adaptive Video Coding for HTTP Adaptive Streaming
Content-adaptive Video Coding for HTTP Adaptive StreamingContent-adaptive Video Coding for HTTP Adaptive Streaming
Content-adaptive Video Coding for HTTP Adaptive Streaming
 
Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video...
Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video...Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video...
Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video...
 
Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Vid...
Empowerment of Atypical Viewers  via Low-Effort Personalized Modeling  of Vid...Empowerment of Atypical Viewers  via Low-Effort Personalized Modeling  of Vid...
Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Vid...
 
Optimizing Video Streaming for Sustainability and Quality: The Role of Prese...
Optimizing Video Streaming  for Sustainability and Quality: The Role of Prese...Optimizing Video Streaming  for Sustainability and Quality: The Role of Prese...
Optimizing Video Streaming for Sustainability and Quality: The Role of Prese...
 
Energy-Efficient Multi-Codec Bitrate-Ladder Estimation for Adaptive Video Str...
Energy-Efficient Multi-Codec Bitrate-Ladder Estimation for Adaptive Video Str...Energy-Efficient Multi-Codec Bitrate-Ladder Estimation for Adaptive Video Str...
Energy-Efficient Multi-Codec Bitrate-Ladder Estimation for Adaptive Video Str...
 
Machine Learning Based Resource Utilization Prediction in the Computing Conti...
Machine Learning Based Resource Utilization Prediction in the Computing Conti...Machine Learning Based Resource Utilization Prediction in the Computing Conti...
Machine Learning Based Resource Utilization Prediction in the Computing Conti...
 
Evaluation of Quality of Experience of ABR Schemes in Gaming Stream
Evaluation of Quality of Experience of ABR Schemes in Gaming StreamEvaluation of Quality of Experience of ABR Schemes in Gaming Stream
Evaluation of Quality of Experience of ABR Schemes in Gaming Stream
 
Network-Assisted Delivery of Adaptive Video Streaming Services through CDN, S...
Network-Assisted Delivery of Adaptive Video Streaming Services through CDN, S...Network-Assisted Delivery of Adaptive Video Streaming Services through CDN, S...
Network-Assisted Delivery of Adaptive Video Streaming Services through CDN, S...
 
Multi-access Edge Computing for Adaptive Video Streaming
Multi-access Edge Computing for Adaptive Video StreamingMulti-access Edge Computing for Adaptive Video Streaming
Multi-access Edge Computing for Adaptive Video Streaming
 
Policy-Driven Dynamic HTTP Adaptive Streaming Player Environment
Policy-Driven Dynamic HTTP Adaptive Streaming Player EnvironmentPolicy-Driven Dynamic HTTP Adaptive Streaming Player Environment
Policy-Driven Dynamic HTTP Adaptive Streaming Player Environment
 
VE-Match: Video Encoding Matching-based Model for Cloud and Edge Computing In...
VE-Match: Video Encoding Matching-based Model for Cloud and Edge Computing In...VE-Match: Video Encoding Matching-based Model for Cloud and Edge Computing In...
VE-Match: Video Encoding Matching-based Model for Cloud and Edge Computing In...
 
Energy Consumption in Video Streaming: Components, Measurements, and Strategies
Energy Consumption in Video Streaming: Components, Measurements, and StrategiesEnergy Consumption in Video Streaming: Components, Measurements, and Strategies
Energy Consumption in Video Streaming: Components, Measurements, and Strategies
 
Exploring the Energy Consumption of Video Streaming: Components, Challenges, ...
Exploring the Energy Consumption of Video Streaming: Components, Challenges, ...Exploring the Energy Consumption of Video Streaming: Components, Challenges, ...
Exploring the Energy Consumption of Video Streaming: Components, Challenges, ...
 
Video Coding Enhancements for HTTP Adaptive Streaming Using Machine Learning
Video Coding Enhancements for HTTP Adaptive Streaming Using Machine LearningVideo Coding Enhancements for HTTP Adaptive Streaming Using Machine Learning
Video Coding Enhancements for HTTP Adaptive Streaming Using Machine Learning
 
Optimizing QoE and Latency of Live Video Streaming Using Edge Computing a...
Optimizing  QoE and Latency of  Live Video Streaming Using  Edge Computing  a...Optimizing  QoE and Latency of  Live Video Streaming Using  Edge Computing  a...
Optimizing QoE and Latency of Live Video Streaming Using Edge Computing a...
 
SARENA: SFC-Enabled Architecture for Adaptive Video Streaming Applications
SARENA: SFC-Enabled Architecture for Adaptive Video Streaming ApplicationsSARENA: SFC-Enabled Architecture for Adaptive Video Streaming Applications
SARENA: SFC-Enabled Architecture for Adaptive Video Streaming Applications
 

Recently uploaded

Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Victor Rentea
 
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Victor Rentea
 
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
?#DUbAI#??##{{(☎️+971_581248768%)**%*]'#abortion pills for sale in dubai@
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Safe Software
 

Recently uploaded (20)

TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
 
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdfRising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
 
Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)
 
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemkeProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
 
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
 
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
 
MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024
 
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherStrategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a Fresher
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
 
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
 
Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...
 
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
 
DBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor Presentation
 
[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf
 
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
 
WSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering DevelopersWSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering Developers
 
Platformless Horizons for Digital Adaptability
Platformless Horizons for Digital AdaptabilityPlatformless Horizons for Digital Adaptability
Platformless Horizons for Digital Adaptability
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 
Artificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : UncertaintyArtificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : Uncertainty
 
presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century education
 

MPEG-21-based Cross-Layer Optimization Techniques for enabling Quality of Experience

  • 1. MPEG-21-based Cross-Layer Optimization Techniques for enabling Quality of Experience Christian Timmerer Klagenfurt University (UNIKLU)  Faculty of Technical Sciences (TEWI) Department of Information Technology (ITEC)  Multimedia Communication (MMC) http://research.timmerer.com  http://blog.timmerer.com  mailto:christian.timmerer@itec.uni-klu.ac.at Acknowledgments: DANAE, ENTHRONE, P2P-Next, ALICANTE projects funded by EC, SCALIPTV/SCALNET funded by FFG, ASSSV funded by FWF and, in particular Michael Eberhard, Ingo Kofler, Robert Kuschnig, Michael Ransburg, Michael Sablatschan, Hermann Hellwagner
  • 2. Outline Background / Introduction Cross-layer designs & optimizations MPEG-21 Digital Item Adaptation How to exploit MPEG-21 for XL optimizations? Cross-Layer Model (XLM) Instantiation of the XLM by utilizing MPEG-21 metadata Cross-Layer Adaptation Decision-Taking Engine (XL-ADTE) Conclusions 2010/01/20 2 Christian Timmerer, Klagenfurt University, Austria
  • 3. Background / Introduction Cross-layer designs Aim: increase QoS/QoEby performing coordinated actions across the network layers => violating the protocol hierarchy and isolation model Approaches: bottom-up or a top-down or jointly optimizing parameters at the different layers Common property: compromising interoperability in favor of performance Increasing the interoperability of cross-layer designs by adopting an open standard – MPEG-21 Digital Item Adaptation – for describing the functional dependencies across network layers 2010/01/20 Christian Timmerer, Klagenfurt University, Austria 3
  • 4. Digital Item Adaptation DIA := syntax and semantics of tools that assist in the adaptation of Digital Items Goals: Satisfy transmission, storage andconsumption constraints as well asQuality of Service (QoS) management Enable transparent access to (distributed)advanced multimedia content by shieldingusers from network and terminal installationissues Codec Format-independent mechanisms that provide support for Digital Item Adaptation in terms of: Resource adaptation Description adaptation Quality of Service management The adaptation engines themselves are non-normative tools 2010/01/20 Christian Timmerer, Klagenfurt University, Austria 4
  • 5.
  • 7.
  • 11.
  • 12.
  • 13. Conditions5 Context-related metadata describes the usage environment in terms of terminal capabilities; network characteristics; user characteristics; natural environment characteristics; e.g., codec capabilities = mp2, ML@MP; available bandwidth=1500kbps; visually impaired; high-level ambient noise;
  • 14. AdaptationQoS and Universal Constraints Description Content-related metadata – AdaptationQoS– describes the relationship between constraints; feasible adaptation operations satisfying these constraints; associated utilities (qualities); e.g., available bandwidth is 384kbps, terminal display is CIF; reduce bit-rate; quality at QCIF/30fps/QP=10 versus CIF/10fps/QP=15e.g., bit-rate = 256kbps, frame-rate=30fps, resolution=CIF, etc. Universal Constraints Description (UCD): mathematical approach based on an optimization problem find values for the variables representing adaptation parameters that do not violate the limitation constraints (feasibility) and maximize the optimization constraint(optimality, objective function) 2010/01/20 Christian Timmerer, Klagenfurt University, Austria 6
  • 15. How to exploit MPEG-21 for XL optimizations? Three-step approach Cross-Layer Model (XLM): describing the relationship between QoS metrics at different levels No specific notation (e.g., graphical) For example: Instantiation of the XLM by utilizing MPEG-21 metadata AdaptationQoS (AQoS): describe the relationship between constraints, feasible adaptation operations satisfying these constraints, and associated utilities (qualities) Usage Environment Description (UED): context information (network conditions, terminal capabilities, user preferences, etc.) Universal Constraints Description (UCD): limitation and optimization constraints Cross-Layer Adaptation Decision-Taking Engine (XL-ADTE) Software module solving an optimization problem adopting any algorithm 2010/01/20 Christian Timmerer, Klagenfurt University, Austria 7
  • 16. Example: Adaptive XL-based Streaming 2010/01/20 Christian Timmerer, Klagenfurt University, Austria 8 supported display resolution, frame-rate TID, DID, QID packet loss, jitter max. payload size, forward error correction signal strength, physical rate Basic Cross-Layer Model temporal id (TID) dependency id (DID) quality id (QID) packet size vertical and horizontal resolution bit rate frame rate
  • 17. Advanced Cross-Layer Model Desirable characteristics TCP friendliness: long-term throughput similar to TCP Responsiveness: time to act upon a certain event Smoothness: variation experienced for a particular flow TCP-friendly Rate Control Protocol (TFRC) Throughput T in bytes/sec is modeled as a function of Segment size sin bytes RTT estimate r in seconds Loss event rate pas a fraction between 0.0 and 1.0 TCP retransmission timeout value tRTOin seconds (simple tRTO= 4r) Adapts sending rate accordingly If Tcurr > Tnewthen reduce rate else increase rate 2010/01/20 Christian Timmerer, Klagenfurt University, Austria 9
  • 18. Instantiation of XLM using MPEG-21 AdaptationQoS (AQoS) Parameters (TID, …) as IOPins Basic XL model as Look-Up Table (LUT) Advanced XL model as Stack Function (SF) Usage Environment Description (UED) Display resolution as display capabilities Max bit-rate of codec as codec capabilities RTT as packetTwoWay Loss event rate by using the packetLossRate Universal Constraints Description (UCD) Limit constraints resulting bit-rate < TFRC transmit rate resulting bit-rate < max bit-rate of codec video resolution < display size Optimization constraint: max bit-rate 2010/01/20 Christian Timmerer, Klagenfurt University, Austria 10
  • 19. Cross-Layer Adaptation Decision-Taking Engine (XL-ADTE)Example: Adaptation of Scalable Video MPEG/ITU-T Scalable Video Coding (SVC) 3 dimensions of scalability: spatial, temporal, signal-to-noise ratio (SNR) Spatial dimension [pixels]: 640x360, 1024x576, 1920x1080 Temporal dimension [fps]: 15, 30 Step 1: Determine Variables 2010/01/20 Christian Timmerer, Klagenfurt University, Austria 11 Table 1. (a) Adaptation Parameter Variables; (b) Content Property Variables.
  • 20. Example: Adaptation of Scalable Video (cont’d) Step 2: Identify Functional Dependencies Step 3: Restrict Solution Space (Limit Constraints) Step 4: Define Objective Function maximize FrameRate 2010/01/20 Christian Timmerer, Klagenfurt University, Austria 12
  • 21. Example: Adaptation of Scalable Video (cont’d) Possible adaptation parameters Feasible adaptation parameters Optimal adaptation parameters 2010/01/20 Christian Timmerer, Klagenfurt University, Austria 13
  • 22. Conclusions Three steps to cross-layer interoperability Cross-Layer Model (XLM): describing the relationship between QoS metrics at different levels Instantiation of the XLM by utilizing MPEG-21 metadata Cross-Layer Adaptation Decision-Taking Engine (XL-ADTE) 2010/01/20 Christian Timmerer, Klagenfurt University, Austria 14
  • 23. Thank you for your attention ... questions, comments, etc. are welcome … Ass.-Prof. Dipl.-Ing. Dr. Christian Timmerer Klagenfurt University, Department of Information Technology (ITEC) Universitätsstrasse 65-67, A-9020 Klagenfurt, AUSTRIA christian.timmerer@itec.uni-klu.ac.at http://research.timmerer.com/ Tel: +43/463/2700 3621 Fax: +43/463/2700 3699 © Copyright: Christian Timmerer 15 2010/01/20 Christian Timmerer, Klagenfurt University, Austria