SlideShare uma empresa Scribd logo
1 de 18
Ultra-High-Definition Quality of
Experience with MPEG-DASH
Priv.-Doz. Dr. Christian Timmerer
Daniel Weinberger, Christopher Mueller, and Stefan Lederer
Chief Innovation Officer (CIO) at bitmovin GmbH
http://www.bitmovin.com  christian.timmerer@bitmovin.com
Alpen-Adria-Universität Klagenfurt (AAU)  Faculty of Technical Sciences (TEWI)  Department of Information
Technology (ITEC)  Multimedia Communication (MMC)  Sensory Experience Lab (SELab)
http://blog.timmerer.com  http://selab.itec.aau.at/  http://dash.itec.aau.at  christian.timmerer@itec.aau.at
http://www.slideshare.net/christian.timmerer
Outline
• Introduction
• Quality, Quality of Experience, and DASH
• Evaluation strategies
• Results
• Conclusions
• Acknowledgment [some slides]: Ali C. Begen, CISCO
April 16, 2015 NAB2015 - BEC - QoE-DASH 2
Introduction
• Real-time entertainment
– Streaming video and audio
– > 60% of Internet traffic
• All delivered over-the-top (OTT)
• MPEG Dynamic Adaptive
Streaming over HTTP (DASH)
– Coding format agnostic
• DASH Industry Forum
– Interoperability Points (IOPs) for
common codecs and others (v3.0)
– E.g., AVC/H.264, HEVC/H.265, and
AAC
April 16, 2015 NAB2015 - BEC - QoE-DASH 3
Over-The-Top – Adaptive Media Streaming
April 16, 2015 NAB2015 - BEC - QoE-DASH 4
Adaptation logic is within the
client, not normatively specified
by the standard, subject to
research and development
Open Digital Media Value Chain
April 16, 2015 NAB2015 - BEC - QoE-DASH 5
Create
Content
Aggregate
Monetize
Distribute
Content
Consume
Content
Any Content Any Storefront Any Network Any Device
CDNsMedia
Protocols
Internet
Transport
DRM
Encoding
Encapsulation
Dynamic
Ads
Clients
Simplified Example Workflow: bitcodin/bitdash
April 16, 2015 NAB2015 - BEC - QoE-DASH 6
Source: http://www.bitmovin.net/bitcodin-cloud-based-transcoding-streaming-platform/
Internet TV vs. Traditional TV in 2010
• Areas most important to
overall TV experience are
– Content
– Timing control
– Quality
– Ease of use
• While traditional TV surpasses
Internet TV only in quality, it
delivers better “overall
experience”
April 16, 2015 NAB2015 - BEC - QoE-DASH 7
When comparing traditional and Internet TV,
which option is better?
Traditional Internet
Content 7%  79%
Timing / Control 7%  83%
Quality  80% 16%
Ease of Use 23%  52%
Control (FF, etc.) 9%  77%
Portability 4%  92%
Interactivity 31%  52%
Sharing 33%  56%
Overall Experience  53% 33%
Source: Cisco IBSG Youth Survey, Cisco IBSG Youth Focus Group Sessions, 2010
Quality (of Experience)
• QoE as evolution of QoS [ITU-T
P.10/G.100]
• QoS: totality of characteristics
of a telecommunications
service that bear on its ability
to satisfy stated and implied
needs of the user of the service
• QoE: the overall acceptability
of an application or service, as
perceived subjectively by the
end-user
April 16, 2015 NAB2015 - BEC - QoE-DASH 8
Many definitions but in general, it’s like an elephant
Quality of Experience
• COST Action IC1003 – QUALINET (http://www.qualinet.eu/)
“the degree of delight or annoyance of the user of an application or service. It results
from the fulfillment of his or her expectations with respect to the utility and/or
enjoyment of the application or service in the light of the user’s personality and
current state”
• QoE influence factors
– Any characteristic of a user, system, service, application, or context
– Grouped into human, system, and context
• QoE features
– Perceivable, recognized and namable characteristic of the individual’s experience
– Depends on the level of direct perception, interaction, the usage situation
April 16, 2015 NAB2015 - BEC - QoE-DASH 9
QoE for DASH
• Different application domains have different QoE
requirements
– Need to provide specializations of the general QoE definition
– Take into account requirements formulated by means of
influence factors and features of QoE
• QoE influence factors for DASH
– Initial/start-up delay (low)
– Buffer underruns, stalls, freezes (zero)
– Quality switches (low)
– Media throughput (high)
– …
April 16, 2015 NAB2015 - BEC - QoE-DASH 10
QoE Evaluation for DASH-based Services
• Test sequence
– Many datasets available
– Adopted Big Buck Bunny & DASHed it with bitcodin
• Players
– bitdash
– …and compare it with ten different adaptation algorithms
• Objective evaluation
– Test setup
– Predefined bandwidth trajectory (or real network traces)
• Subjective evaluation
– Lab vs. crowdsourcing
April 16, 2015 NAB2015 - BEC - QoE-DASH 11
http://www.bitcodin.com/
http://www.dash-player.com/
Objective Evaluations
April 16, 2015 NAB2015 - BEC - QoE-DASH 12
Stalls (lower is better)Average Bitrate (higher is better)
Stalls are really bad…
April 16, 2015 NAB2015 - BEC - QoE-DASH 13
Conviva: Viewer Experience Report. 2014
DASH-JS vs. bitdash
April 16, 2015 NAB2015 - BEC - QoE-DASH 14
Subjective Evaluation
• Microworker platform
– Limited to Europe, USA/Canada, India
• DASH clients
– DASH-JS (dash.itec.aau.at)
– dash.js (DASH-IF)
– YouTube
• Tears of Steal trailer according to YouTube
configuration
• Screening techniques
– Browser fingerprinting
– Presentation time
– QoE ratings and Pre-Questionnaire
April 16, 2015 NAB2015 - BEC - QoE-DASH 15
What about 4K and 8K?
April 16, 2015 NAB2015 - BEC - QoE-DASH 16
• Why? – because we can!
• Supported on the Web
– HTML5, MSE
– AVC/H.264
– [HEVC/H.265 needed
to lower bitrate]
• See demo @
http://www.dash-
player.com/
• UHD-QoE evaluation
Conclusions
• QoE for DASH-based services (a rule of thumb)
– Startup delay (low [but live vs. on-demand & short vs. long-tail
content])
– Buffer underrun / stalls (zero)
– Quality switches (low) and media throughput (high)
– Energy- and cost-awareness (data plan)
• No general applicable QoE model for DASH
– (Too) many factors influencing / features of QoE for DASH-based
services
– Methodology for reproducible research is in place and well established
– Ample research opportunities
April 16, 2015 NAB2015 - BEC - QoE-DASH 17
Main QoE
factors for DASH
Thank you!
April 16, 2015 NAB2015 - BEC - QoE-DASH 18
Source: http://www.bitmovin.net/bitcodin-cloud-based-transcoding-streaming-platform/

Mais conteúdo relacionado

Mais de 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
 

Mais de Alpen-Adria-Universität (20)

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
 
Immersive Video Delivery: From Omnidirectional Video to Holography
Immersive Video Delivery: From Omnidirectional Video to HolographyImmersive Video Delivery: From Omnidirectional Video to Holography
Immersive Video Delivery: From Omnidirectional Video to Holography
 

Último

+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
 

Último (20)

Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
 
AXA XL - Insurer Innovation Award Americas 2024
AXA XL - Insurer Innovation Award Americas 2024AXA XL - Insurer Innovation Award Americas 2024
AXA XL - Insurer Innovation Award Americas 2024
 
FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024
 
Cyberprint. Dark Pink Apt Group [EN].pdf
Cyberprint. Dark Pink Apt Group [EN].pdfCyberprint. Dark Pink Apt Group [EN].pdf
Cyberprint. Dark Pink Apt Group [EN].pdf
 
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of Terraform
 
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...
 
+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...
 
Exploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with MilvusExploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with Milvus
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
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
 
Manulife - Insurer Transformation Award 2024
Manulife - Insurer Transformation Award 2024Manulife - Insurer Transformation Award 2024
Manulife - Insurer Transformation Award 2024
 
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
 
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
 
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
 
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ..."I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
 
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
 
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
 
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...
 
CNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In PakistanCNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In Pakistan
 

Ultra-High-Definition Quality of Experience with MPEG-DASH

  • 1. Ultra-High-Definition Quality of Experience with MPEG-DASH Priv.-Doz. Dr. Christian Timmerer Daniel Weinberger, Christopher Mueller, and Stefan Lederer Chief Innovation Officer (CIO) at bitmovin GmbH http://www.bitmovin.com  christian.timmerer@bitmovin.com Alpen-Adria-Universität Klagenfurt (AAU)  Faculty of Technical Sciences (TEWI)  Department of Information Technology (ITEC)  Multimedia Communication (MMC)  Sensory Experience Lab (SELab) http://blog.timmerer.com  http://selab.itec.aau.at/  http://dash.itec.aau.at  christian.timmerer@itec.aau.at http://www.slideshare.net/christian.timmerer
  • 2. Outline • Introduction • Quality, Quality of Experience, and DASH • Evaluation strategies • Results • Conclusions • Acknowledgment [some slides]: Ali C. Begen, CISCO April 16, 2015 NAB2015 - BEC - QoE-DASH 2
  • 3. Introduction • Real-time entertainment – Streaming video and audio – > 60% of Internet traffic • All delivered over-the-top (OTT) • MPEG Dynamic Adaptive Streaming over HTTP (DASH) – Coding format agnostic • DASH Industry Forum – Interoperability Points (IOPs) for common codecs and others (v3.0) – E.g., AVC/H.264, HEVC/H.265, and AAC April 16, 2015 NAB2015 - BEC - QoE-DASH 3
  • 4. Over-The-Top – Adaptive Media Streaming April 16, 2015 NAB2015 - BEC - QoE-DASH 4 Adaptation logic is within the client, not normatively specified by the standard, subject to research and development
  • 5. Open Digital Media Value Chain April 16, 2015 NAB2015 - BEC - QoE-DASH 5 Create Content Aggregate Monetize Distribute Content Consume Content Any Content Any Storefront Any Network Any Device CDNsMedia Protocols Internet Transport DRM Encoding Encapsulation Dynamic Ads Clients
  • 6. Simplified Example Workflow: bitcodin/bitdash April 16, 2015 NAB2015 - BEC - QoE-DASH 6 Source: http://www.bitmovin.net/bitcodin-cloud-based-transcoding-streaming-platform/
  • 7. Internet TV vs. Traditional TV in 2010 • Areas most important to overall TV experience are – Content – Timing control – Quality – Ease of use • While traditional TV surpasses Internet TV only in quality, it delivers better “overall experience” April 16, 2015 NAB2015 - BEC - QoE-DASH 7 When comparing traditional and Internet TV, which option is better? Traditional Internet Content 7%  79% Timing / Control 7%  83% Quality  80% 16% Ease of Use 23%  52% Control (FF, etc.) 9%  77% Portability 4%  92% Interactivity 31%  52% Sharing 33%  56% Overall Experience  53% 33% Source: Cisco IBSG Youth Survey, Cisco IBSG Youth Focus Group Sessions, 2010
  • 8. Quality (of Experience) • QoE as evolution of QoS [ITU-T P.10/G.100] • QoS: totality of characteristics of a telecommunications service that bear on its ability to satisfy stated and implied needs of the user of the service • QoE: the overall acceptability of an application or service, as perceived subjectively by the end-user April 16, 2015 NAB2015 - BEC - QoE-DASH 8 Many definitions but in general, it’s like an elephant
  • 9. Quality of Experience • COST Action IC1003 – QUALINET (http://www.qualinet.eu/) “the degree of delight or annoyance of the user of an application or service. It results from the fulfillment of his or her expectations with respect to the utility and/or enjoyment of the application or service in the light of the user’s personality and current state” • QoE influence factors – Any characteristic of a user, system, service, application, or context – Grouped into human, system, and context • QoE features – Perceivable, recognized and namable characteristic of the individual’s experience – Depends on the level of direct perception, interaction, the usage situation April 16, 2015 NAB2015 - BEC - QoE-DASH 9
  • 10. QoE for DASH • Different application domains have different QoE requirements – Need to provide specializations of the general QoE definition – Take into account requirements formulated by means of influence factors and features of QoE • QoE influence factors for DASH – Initial/start-up delay (low) – Buffer underruns, stalls, freezes (zero) – Quality switches (low) – Media throughput (high) – … April 16, 2015 NAB2015 - BEC - QoE-DASH 10
  • 11. QoE Evaluation for DASH-based Services • Test sequence – Many datasets available – Adopted Big Buck Bunny & DASHed it with bitcodin • Players – bitdash – …and compare it with ten different adaptation algorithms • Objective evaluation – Test setup – Predefined bandwidth trajectory (or real network traces) • Subjective evaluation – Lab vs. crowdsourcing April 16, 2015 NAB2015 - BEC - QoE-DASH 11 http://www.bitcodin.com/ http://www.dash-player.com/
  • 12. Objective Evaluations April 16, 2015 NAB2015 - BEC - QoE-DASH 12 Stalls (lower is better)Average Bitrate (higher is better)
  • 13. Stalls are really bad… April 16, 2015 NAB2015 - BEC - QoE-DASH 13 Conviva: Viewer Experience Report. 2014
  • 14. DASH-JS vs. bitdash April 16, 2015 NAB2015 - BEC - QoE-DASH 14
  • 15. Subjective Evaluation • Microworker platform – Limited to Europe, USA/Canada, India • DASH clients – DASH-JS (dash.itec.aau.at) – dash.js (DASH-IF) – YouTube • Tears of Steal trailer according to YouTube configuration • Screening techniques – Browser fingerprinting – Presentation time – QoE ratings and Pre-Questionnaire April 16, 2015 NAB2015 - BEC - QoE-DASH 15
  • 16. What about 4K and 8K? April 16, 2015 NAB2015 - BEC - QoE-DASH 16 • Why? – because we can! • Supported on the Web – HTML5, MSE – AVC/H.264 – [HEVC/H.265 needed to lower bitrate] • See demo @ http://www.dash- player.com/ • UHD-QoE evaluation
  • 17. Conclusions • QoE for DASH-based services (a rule of thumb) – Startup delay (low [but live vs. on-demand & short vs. long-tail content]) – Buffer underrun / stalls (zero) – Quality switches (low) and media throughput (high) – Energy- and cost-awareness (data plan) • No general applicable QoE model for DASH – (Too) many factors influencing / features of QoE for DASH-based services – Methodology for reproducible research is in place and well established – Ample research opportunities April 16, 2015 NAB2015 - BEC - QoE-DASH 17 Main QoE factors for DASH
  • 18. Thank you! April 16, 2015 NAB2015 - BEC - QoE-DASH 18 Source: http://www.bitmovin.net/bitcodin-cloud-based-transcoding-streaming-platform/