SlideShare a Scribd company logo
1 of 17
AC M M u l t i m e d i a
  Syste m s 2 0 1 2




                             Dynamic Adaptive Streaming over HTTP (DASH)
                                               Dataset

                             Stefan Lederer, Christopher Müller and Christian
Feb. 22-24
   2012                                         Timmerer
Chapel Hill                           Alpen-Adria-Universität Klagenfurt (AAU)
Motivation
       • HTTP Streaming has become very popular on the
         Internet
             – Various different bitrates of the video are available on the
               server --> choose the best version to load
             – Easy to use existing CDN structure
             – No NAT/Firewall issues due to HTTP
             – Various technologies


       • BUT: no standard in use!

ACM MMSys 2012, Feb. 22-24, Chapel Hill/NC/USA                                Slide 2
DASH

       • Dynamic Adaptive Streaming over HTTP
         (DASH)
             – Will be Part 6 of MPEG-B
             – Existing DASH Plugin for VLC




ACM MMSys 2012, Feb. 22-24, Chapel Hill/NC/USA   Slide 3
Dataset
       • Dataset with DASH Content
             –   Long sequences in high quality
             –   Various segment-length versions
             –   Free available for DASH experiments
             –   PSNR values per frame


       • Problem: Content Rights
             – CC-Attribution 2.0 Generic (CC-BY 2.0) License or similar
             – Free to Share, Free to Remix
             – Note: YouTube introduces CC-BY in June 2011!


       • Negotiation with content owner

ACM MMSys 2012, Feb. 22-24, Chapel Hill/NC/USA                             Slide 4
Dataset Sequences

                  Name                             Source Quality      Length      Genre

          Big Buck Bunny                             1080p YUV          09:46     Animation

         Elephants Dream                             1080p YUV          10:54     Animation

       Red Bull Playstreets                      1080p, 6 Mbit H.264   01:37:28     Sport

        The Swiss Account                        1080p, 6 Mbit H.264    57:34       Sport

               Valkaama                          1080p, 6 Mbit H.264   01:33:05    Movie

        Of Forest and Men                                SD             10:53      Movie


ACM MMSys 2012, Feb. 22-24, Chapel Hill/NC/USA                                          Slide 5
DASH Dataset Sequences




ACM MMSys 2012, Feb. 22-24, Chapel Hill/NC/USA   Slide 6
Bitrates and Resolutions
   #                 Animation                           Sport                  Movie
   1           50 kbit/s, 320x240                 100 kbit/s, 320x240     50 kbit/s, 320x240
   2          100 kbit/s, 320x240                 150 kbit/s, 320x240    100 kbit/s, 320x240
   3          150 kbit/s, 320x240                 200 kbit/s, 480x360    150 kbit/s, 320x240
   4          200 kbit/s, 480x360                 250 kbit/s, 480x360    200 kbit/s, 480x360
   5          250 kbit/s, 480x360                 300 kbit/s, 480x360    250 kbit/s, 480x360
   6          300 kbit/s, 480x360                 400 kbit/s, 480x360    300 kbit/s, 480x360
   7          400 kbit/s, 480x360                 500 kbit/s, 854x480    400 kbit/s, 480x360
   8          500 kbit/s, 480x360                 700 kbit/s, 854x480    500 kbit/s, 854x480
   9          600 kbit/s, 854x480                 900 kbit/s, 854x480    600 kbit/s, 854x480
  10          700 kbit/s, 854x480                1,2 Mbit/s, 854x480     700 kbit/s, 854x480
  11          900 kbit/s,1280x720                 1,5 Mbit/s,1280x720    900 kbit/s,1280x720
  12          1,2 Mbit/s,1280x720                 2,0 Mbit/s,1280x720    1,2 Mbit/s,1280x720
  13          1,5 Mbit/s,1280x720                 2,5 Mbit/s,1280x720    1,5 Mbit/s,1280x720
  14          2,0 Mbit/s,1280x720                3,0 Mbit/s,1920x1080   2,0 Mbit/s,1920x1080
  15         2,5 Mbit/s,1920x1080                4,0 Mbit/s,1920x1080   2,5 Mbit/s,1920x1080
  16         3,0 Mbit/s,1920x1080                5,0 Mbit/s,1920x1080   3,0 Mbit/s,1920x1080
  17         4,0 Mbit/s,1920x1080                6,0 Mbit/s,1920x1080   4,0 Mbit/s,1920x1080
  18         5,0 Mbit/s,1920x1080                                       5,0 Mbit/s,1920x1080
  19         6,0 Mbit/s,1920x1080                                       6,0 Mbit/s,1920x1080
  20         8,0 Mbit/s,1920x1080
ACM MMSys 2012, Feb. 22-24, Chapel Hill/NC/USA                                            Slide 7
DASH Content Types

       • Segment Size:
             – Seconds: 1, 2, 4, 6, 10, 15
       • File Organization
             – Segmented
             – One file per representation, Byte Range Requests
       • e.g.: Big Buck Bunny
             – 120 Encodings needed
             – Only 6 DASH Encoder runs
ACM MMSys 2012, Feb. 22-24, Chapel Hill/NC/USA                    Slide 8
DASHEncoder

       • DASH Content Generation Tool
             – Encoding + Multiplexing + MPD generation
             – Generates isoffmain profile compliant MPDs
             – Fully configurable using a config-file
             – Enables batch processing
             – Currently uses x264 and GPAC‘s MP4Box
             – Easy extensible to further
               encoders & multiplexers

ACM MMSys 2012, Feb. 22-24, Chapel Hill/NC/USA              Slide 9
DASH Encoder

                         • h.264:       x264 / ffmpeg
                         • AAC:         ffmpeg
         Encode          • [WebM, etc.]



                         • MP4Box:     Video / Audio / Video + Audio
                         • [e.g. WebM/MKV Segmenter]
       Container


                         • Generate one MPD
                         • Subfolder Organization
           MPD           • MPD Transformation



ACM MMSys 2012, Feb. 22-24, Chapel Hill/NC/USA                         Slide 10
Connection Type Evaluation

       • Peristent vs. non-persisten connections
             – Bandwidth variations in high delay network
             – Influence of segment length to streaming
               performance
             – Apache Web Server + DASH VLC Plugin (AAU)




ACM MMSys 2012, Feb. 22-24, Chapel Hill/NC/USA              Slide 11
Non-/Persistent Conn.
       Segment Length: 2 sec.




ACM MMSys 2012, Feb. 22-24, Chapel Hill/NC/USA   Slide 12
Non-/Persistent Conn.
       Segment Length: 15 sec.




ACM MMSys 2012, Feb. 22-24, Chapel Hill/NC/USA   Slide 13
Evaluation Results




ACM MMSys 2012, Feb. 22-24, Chapel Hill/NC/USA   Slide 14
Conclusion
       • Public available and free DASH dataset
       • Common basis for evaluations
             – DASH Implementations, Stream Switching Algorithms,
               Network and Cache Configurations, ...
             – Enables objective comparison of research results
       • Basic content generation tool: DASHEncoder
       • Fully compatible to DASH VLC Plugin of ITEC/AAU
       • Evaluation showing influence of segment length


ACM MMSys 2012, Feb. 22-24, Chapel Hill/NC/USA                      Slide 15
Future Work
       • Dataset
             – Further profiles and MPDs
             – Further media segment formats
       • Evaluation of mobile scenarios
             – Under vehicular & pedestrian mobility
       • Peer-assisted DASH
             – Reduce server bandwidth requirements
             – Inter-peer synchronisation & communication


ACM MMSys 2012, Feb. 22-24, Chapel Hill/NC/USA              Slide 16
Stefan Lederer
                                       Christopher Müller
                                       Christian Timmerer




                          Thank You!

http://dash.itec.aau.at

More Related Content

What's hot

Towards Peer-Assisted Dynamic Adaptive Streaming over HTTP
Towards Peer-Assisted Dynamic Adaptive Streaming over HTTPTowards Peer-Assisted Dynamic Adaptive Streaming over HTTP
Towards Peer-Assisted Dynamic Adaptive Streaming over HTTPStefan Lederer / bitmovin.net
 
Using SVC for DASH in Mobile Environments
Using SVC for DASH in Mobile EnvironmentsUsing SVC for DASH in Mobile Environments
Using SVC for DASH in Mobile EnvironmentsChristopher Mueller
 
Adaptive Media Streaming over Emerging Protocols
Adaptive Media Streaming over Emerging ProtocolsAdaptive Media Streaming over Emerging Protocols
Adaptive Media Streaming over Emerging ProtocolsAlpen-Adria-Universität
 
A PROXY EFFECT ANALYIS AND FAIR ADATPATION ALGORITHM FOR MULTIPLE COMPETING D...
A PROXY EFFECT ANALYIS AND FAIR ADATPATION ALGORITHM FOR MULTIPLE COMPETING D...A PROXY EFFECT ANALYIS AND FAIR ADATPATION ALGORITHM FOR MULTIPLE COMPETING D...
A PROXY EFFECT ANALYIS AND FAIR ADATPATION ALGORITHM FOR MULTIPLE COMPETING D...Christopher Mueller
 
Standards' Perspective - MPEG DASH overview and related efforts
Standards' Perspective - MPEG DASH overview and related effortsStandards' Perspective - MPEG DASH overview and related efforts
Standards' Perspective - MPEG DASH overview and related effortsIMTC
 
Dynamic Adaptive Streaming over HTTP: From Content Creation to Consumption
Dynamic Adaptive Streaming over HTTP: From Content Creation to ConsumptionDynamic Adaptive Streaming over HTTP: From Content Creation to Consumption
Dynamic Adaptive Streaming over HTTP: From Content Creation to ConsumptionAlpen-Adria-Universität
 
Ebu mpeg dash-webinar043
Ebu mpeg dash-webinar043Ebu mpeg dash-webinar043
Ebu mpeg dash-webinar043mc_killah
 
Edge 2014: MPEG DASH – Tomorrow's Format Today
Edge 2014: MPEG DASH – Tomorrow's Format TodayEdge 2014: MPEG DASH – Tomorrow's Format Today
Edge 2014: MPEG DASH – Tomorrow's Format TodayAkamai Technologies
 
Using DASH and MPEG-2 TS
Using DASH and MPEG-2 TSUsing DASH and MPEG-2 TS
Using DASH and MPEG-2 TSAlex Giladi
 
Video Coding Enhancements for HTTP Adaptive Streaming
Video Coding Enhancements for HTTP Adaptive StreamingVideo Coding Enhancements for HTTP Adaptive Streaming
Video Coding Enhancements for HTTP Adaptive StreamingAlpen-Adria-Universität
 
Chapter7 multimedia
Chapter7 multimediaChapter7 multimedia
Chapter7 multimediaKhánh Ghẻ
 
HTTP Adaptive Streaming State of the Art and Challenges Ahead
HTTP Adaptive StreamingState of the Art and Challenges AheadHTTP Adaptive StreamingState of the Art and Challenges Ahead
HTTP Adaptive Streaming State of the Art and Challenges AheadAlpen-Adria-Universität
 
Analysis of Adaptive Streaming for Hybrid CDN/P2P Live Video Systems
Analysis of Adaptive Streaming for Hybrid CDN/P2P Live Video SystemsAnalysis of Adaptive Streaming for Hybrid CDN/P2P Live Video Systems
Analysis of Adaptive Streaming for Hybrid CDN/P2P Live Video SystemsKevin Tong
 
Understanding MPEG DASH
Understanding MPEG DASHUnderstanding MPEG DASH
Understanding MPEG DASHSeung-Bum Lee
 

What's hot (20)

Towards Peer-Assisted Dynamic Adaptive Streaming over HTTP
Towards Peer-Assisted Dynamic Adaptive Streaming over HTTPTowards Peer-Assisted Dynamic Adaptive Streaming over HTTP
Towards Peer-Assisted Dynamic Adaptive Streaming over HTTP
 
Using SVC for DASH in Mobile Environments
Using SVC for DASH in Mobile EnvironmentsUsing SVC for DASH in Mobile Environments
Using SVC for DASH in Mobile Environments
 
Distributed DASH Dataset
Distributed DASH DatasetDistributed DASH Dataset
Distributed DASH Dataset
 
Adaptive Media Streaming over Emerging Protocols
Adaptive Media Streaming over Emerging ProtocolsAdaptive Media Streaming over Emerging Protocols
Adaptive Media Streaming over Emerging Protocols
 
HTTP Streaming of MPEG Media
HTTP Streaming of MPEG MediaHTTP Streaming of MPEG Media
HTTP Streaming of MPEG Media
 
A PROXY EFFECT ANALYIS AND FAIR ADATPATION ALGORITHM FOR MULTIPLE COMPETING D...
A PROXY EFFECT ANALYIS AND FAIR ADATPATION ALGORITHM FOR MULTIPLE COMPETING D...A PROXY EFFECT ANALYIS AND FAIR ADATPATION ALGORITHM FOR MULTIPLE COMPETING D...
A PROXY EFFECT ANALYIS AND FAIR ADATPATION ALGORITHM FOR MULTIPLE COMPETING D...
 
Standards' Perspective - MPEG DASH overview and related efforts
Standards' Perspective - MPEG DASH overview and related effortsStandards' Perspective - MPEG DASH overview and related efforts
Standards' Perspective - MPEG DASH overview and related efforts
 
DASH at the ACM Multimedia 2011
DASH at the ACM Multimedia 2011DASH at the ACM Multimedia 2011
DASH at the ACM Multimedia 2011
 
Dynamic Adaptive Streaming over HTTP: From Content Creation to Consumption
Dynamic Adaptive Streaming over HTTP: From Content Creation to ConsumptionDynamic Adaptive Streaming over HTTP: From Content Creation to Consumption
Dynamic Adaptive Streaming over HTTP: From Content Creation to Consumption
 
MPEG-DASH open source tools and cloud services
MPEG-DASH open source tools and cloud servicesMPEG-DASH open source tools and cloud services
MPEG-DASH open source tools and cloud services
 
Ebu mpeg dash-webinar043
Ebu mpeg dash-webinar043Ebu mpeg dash-webinar043
Ebu mpeg dash-webinar043
 
Edge 2014: MPEG DASH – Tomorrow's Format Today
Edge 2014: MPEG DASH – Tomorrow's Format TodayEdge 2014: MPEG DASH – Tomorrow's Format Today
Edge 2014: MPEG DASH – Tomorrow's Format Today
 
Using DASH and MPEG-2 TS
Using DASH and MPEG-2 TSUsing DASH and MPEG-2 TS
Using DASH and MPEG-2 TS
 
ProjectReportSem2
ProjectReportSem2ProjectReportSem2
ProjectReportSem2
 
Video Coding Enhancements for HTTP Adaptive Streaming
Video Coding Enhancements for HTTP Adaptive StreamingVideo Coding Enhancements for HTTP Adaptive Streaming
Video Coding Enhancements for HTTP Adaptive Streaming
 
Chapter7 multimedia
Chapter7 multimediaChapter7 multimedia
Chapter7 multimedia
 
HTTP Adaptive Streaming State of the Art and Challenges Ahead
HTTP Adaptive StreamingState of the Art and Challenges AheadHTTP Adaptive StreamingState of the Art and Challenges Ahead
HTTP Adaptive Streaming State of the Art and Challenges Ahead
 
Analysis of Adaptive Streaming for Hybrid CDN/P2P Live Video Systems
Analysis of Adaptive Streaming for Hybrid CDN/P2P Live Video SystemsAnalysis of Adaptive Streaming for Hybrid CDN/P2P Live Video Systems
Analysis of Adaptive Streaming for Hybrid CDN/P2P Live Video Systems
 
An Introduction to OMNeT++ 5.4
An Introduction to OMNeT++ 5.4An Introduction to OMNeT++ 5.4
An Introduction to OMNeT++ 5.4
 
Understanding MPEG DASH
Understanding MPEG DASHUnderstanding MPEG DASH
Understanding MPEG DASH
 

Similar to MPEG-DASH Dataset MMSys 2012

Multi-Criteria Optimization of Content Delivery within the Future Media Internet
Multi-Criteria Optimization of Content Delivery within the Future Media InternetMulti-Criteria Optimization of Content Delivery within the Future Media Internet
Multi-Criteria Optimization of Content Delivery within the Future Media Internetjbruneauqueyreix
 
06-dash.pptx
06-dash.pptx06-dash.pptx
06-dash.pptxAliIssa53
 
5 16-12 curated series #2 presentation
5 16-12 curated series #2 presentation5 16-12 curated series #2 presentation
5 16-12 curated series #2 presentationDuraSpace
 
1_MWS2018_Tutorial1_Pham_Internet Delivered Media.pdf
1_MWS2018_Tutorial1_Pham_Internet Delivered Media.pdf1_MWS2018_Tutorial1_Pham_Internet Delivered Media.pdf
1_MWS2018_Tutorial1_Pham_Internet Delivered Media.pdfAliIssa53
 
Compression Synopsis H264-H265
Compression Synopsis H264-H265Compression Synopsis H264-H265
Compression Synopsis H264-H265Paul Hightower
 
Adaptive Streaming of Traditional and Omnidirectional Media
Adaptive Streaming of Traditional and Omnidirectional MediaAdaptive Streaming of Traditional and Omnidirectional Media
Adaptive Streaming of Traditional and Omnidirectional MediaAlpen-Adria-Universität
 
Streaming video to html
Streaming video to htmlStreaming video to html
Streaming video to htmljeff tapper
 
MMSys'21 DS- RezaFarahani.pdf
MMSys'21 DS- RezaFarahani.pdfMMSys'21 DS- RezaFarahani.pdf
MMSys'21 DS- RezaFarahani.pdfReza Farahani
 
Chapter 15 distributed mm systems
Chapter 15 distributed mm systemsChapter 15 distributed mm systems
Chapter 15 distributed mm systemsAbDul ThaYyal
 
QoS for Media Networks
QoS for Media NetworksQoS for Media Networks
QoS for Media NetworksAmine Choukir
 
Tutorial adaptive-streaming
Tutorial adaptive-streamingTutorial adaptive-streaming
Tutorial adaptive-streamingJohnGregory89
 
SDV overview 042706
SDV overview 042706SDV overview 042706
SDV overview 042706owenlin
 
DOTNET 2013 IEEE CLOUDCOMPUTING PROJECT An adaptive cloud downloading service
DOTNET 2013 IEEE CLOUDCOMPUTING PROJECT An adaptive cloud downloading serviceDOTNET 2013 IEEE CLOUDCOMPUTING PROJECT An adaptive cloud downloading service
DOTNET 2013 IEEE CLOUDCOMPUTING PROJECT An adaptive cloud downloading serviceIEEEGLOBALSOFTTECHNOLOGIES
 
The future of tape april 16
The future of tape april 16The future of tape april 16
The future of tape april 16Josef Weingand
 
Quality of Experience of Web-based Adaptive HTTP Streaming Clients in Real-Wo...
Quality of Experience of Web-based Adaptive HTTP Streaming Clients in Real-Wo...Quality of Experience of Web-based Adaptive HTTP Streaming Clients in Real-Wo...
Quality of Experience of Web-based Adaptive HTTP Streaming Clients in Real-Wo...Alpen-Adria-Universität
 
World Cup Webinar from Signiant
World Cup Webinar from SigniantWorld Cup Webinar from Signiant
World Cup Webinar from SigniantSigniant
 
Video Coding for Large-Scale HTTP Adaptive Streaming Deployments: State of th...
Video Coding for Large-Scale HTTP Adaptive Streaming Deployments: State of th...Video Coding for Large-Scale HTTP Adaptive Streaming Deployments: State of th...
Video Coding for Large-Scale HTTP Adaptive Streaming Deployments: State of th...Alpen-Adria-Universität
 
Xevgenis_Michail_CI7120 Multimedia Communications
Xevgenis_Michail_CI7120 Multimedia CommunicationsXevgenis_Michail_CI7120 Multimedia Communications
Xevgenis_Michail_CI7120 Multimedia CommunicationsMichael Xevgenis
 

Similar to MPEG-DASH Dataset MMSys 2012 (20)

Poster @ ACM Multimedia Systems 2012
Poster @ ACM Multimedia Systems 2012Poster @ ACM Multimedia Systems 2012
Poster @ ACM Multimedia Systems 2012
 
Multi-Criteria Optimization of Content Delivery within the Future Media Internet
Multi-Criteria Optimization of Content Delivery within the Future Media InternetMulti-Criteria Optimization of Content Delivery within the Future Media Internet
Multi-Criteria Optimization of Content Delivery within the Future Media Internet
 
06-dash.pptx
06-dash.pptx06-dash.pptx
06-dash.pptx
 
5 16-12 curated series #2 presentation
5 16-12 curated series #2 presentation5 16-12 curated series #2 presentation
5 16-12 curated series #2 presentation
 
1_MWS2018_Tutorial1_Pham_Internet Delivered Media.pdf
1_MWS2018_Tutorial1_Pham_Internet Delivered Media.pdf1_MWS2018_Tutorial1_Pham_Internet Delivered Media.pdf
1_MWS2018_Tutorial1_Pham_Internet Delivered Media.pdf
 
Compression Synopsis H264-H265
Compression Synopsis H264-H265Compression Synopsis H264-H265
Compression Synopsis H264-H265
 
Adaptive Streaming of Traditional and Omnidirectional Media
Adaptive Streaming of Traditional and Omnidirectional MediaAdaptive Streaming of Traditional and Omnidirectional Media
Adaptive Streaming of Traditional and Omnidirectional Media
 
Streaming video to html
Streaming video to htmlStreaming video to html
Streaming video to html
 
MMSys'21 DS- RezaFarahani.pdf
MMSys'21 DS- RezaFarahani.pdfMMSys'21 DS- RezaFarahani.pdf
MMSys'21 DS- RezaFarahani.pdf
 
Chapter 15 distributed mm systems
Chapter 15 distributed mm systemsChapter 15 distributed mm systems
Chapter 15 distributed mm systems
 
QoS for Media Networks
QoS for Media NetworksQoS for Media Networks
QoS for Media Networks
 
Tutorial adaptive-streaming
Tutorial adaptive-streamingTutorial adaptive-streaming
Tutorial adaptive-streaming
 
Dmms
DmmsDmms
Dmms
 
SDV overview 042706
SDV overview 042706SDV overview 042706
SDV overview 042706
 
DOTNET 2013 IEEE CLOUDCOMPUTING PROJECT An adaptive cloud downloading service
DOTNET 2013 IEEE CLOUDCOMPUTING PROJECT An adaptive cloud downloading serviceDOTNET 2013 IEEE CLOUDCOMPUTING PROJECT An adaptive cloud downloading service
DOTNET 2013 IEEE CLOUDCOMPUTING PROJECT An adaptive cloud downloading service
 
The future of tape april 16
The future of tape april 16The future of tape april 16
The future of tape april 16
 
Quality of Experience of Web-based Adaptive HTTP Streaming Clients in Real-Wo...
Quality of Experience of Web-based Adaptive HTTP Streaming Clients in Real-Wo...Quality of Experience of Web-based Adaptive HTTP Streaming Clients in Real-Wo...
Quality of Experience of Web-based Adaptive HTTP Streaming Clients in Real-Wo...
 
World Cup Webinar from Signiant
World Cup Webinar from SigniantWorld Cup Webinar from Signiant
World Cup Webinar from Signiant
 
Video Coding for Large-Scale HTTP Adaptive Streaming Deployments: State of th...
Video Coding for Large-Scale HTTP Adaptive Streaming Deployments: State of th...Video Coding for Large-Scale HTTP Adaptive Streaming Deployments: State of th...
Video Coding for Large-Scale HTTP Adaptive Streaming Deployments: State of th...
 
Xevgenis_Michail_CI7120 Multimedia Communications
Xevgenis_Michail_CI7120 Multimedia CommunicationsXevgenis_Michail_CI7120 Multimedia Communications
Xevgenis_Michail_CI7120 Multimedia Communications
 

More from 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 StreamingAlpen-Adria-Universität
 
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...Alpen-Adria-Universität
 
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...Alpen-Adria-Universität
 
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...Alpen-Adria-Universität
 
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...Alpen-Adria-Universität
 
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...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 StreamAlpen-Adria-Universität
 
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...Alpen-Adria-Universität
 
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 StreamingAlpen-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 EnvironmentAlpen-Adria-Universität
 
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...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 StrategiesAlpen-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
 
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 LearningAlpen-Adria-Universität
 
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...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 ApplicationsAlpen-Adria-Universität
 
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 HolographyAlpen-Adria-Universität
 
LLL-CAdViSE: Live Low-Latency Cloud-based Adaptive Video Streaming Evaluation...
LLL-CAdViSE: Live Low-Latency Cloud-based Adaptive Video Streaming Evaluation...LLL-CAdViSE: Live Low-Latency Cloud-based Adaptive Video Streaming Evaluation...
LLL-CAdViSE: Live Low-Latency Cloud-based Adaptive Video Streaming Evaluation...Alpen-Adria-Universität
 
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
 
How to Optimize Dynamic Adaptive Video Streaming? Challenges and Solutions
How to Optimize Dynamic Adaptive Video Streaming? Challenges and SolutionsHow to Optimize Dynamic Adaptive Video Streaming? Challenges and Solutions
How to Optimize Dynamic Adaptive Video Streaming? Challenges and SolutionsAlpen-Adria-Universität
 

More from Alpen-Adria-Universität (20)

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
 
LLL-CAdViSE: Live Low-Latency Cloud-based Adaptive Video Streaming Evaluation...
LLL-CAdViSE: Live Low-Latency Cloud-based Adaptive Video Streaming Evaluation...LLL-CAdViSE: Live Low-Latency Cloud-based Adaptive Video Streaming Evaluation...
LLL-CAdViSE: Live Low-Latency Cloud-based Adaptive Video Streaming Evaluation...
 
HTTP Adaptive Streaming – Quo Vadis? (2023)
HTTP Adaptive Streaming – Quo Vadis? (2023)HTTP Adaptive Streaming – Quo Vadis? (2023)
HTTP Adaptive Streaming – Quo Vadis? (2023)
 
How to Optimize Dynamic Adaptive Video Streaming? Challenges and Solutions
How to Optimize Dynamic Adaptive Video Streaming? Challenges and SolutionsHow to Optimize Dynamic Adaptive Video Streaming? Challenges and Solutions
How to Optimize Dynamic Adaptive Video Streaming? Challenges and Solutions
 

Recently uploaded

Gen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfGen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfAddepto
 
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptxThe Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptxLoriGlavin3
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenHervé Boutemy
 
Generative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersGenerative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersRaghuram Pandurangan
 
Rise of the Machines: Known As Drones...
Rise of the Machines: Known As Drones...Rise of the Machines: Known As Drones...
Rise of the Machines: Known As Drones...Rick Flair
 
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptxThe Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptxLoriGlavin3
 
Training state-of-the-art general text embedding
Training state-of-the-art general text embeddingTraining state-of-the-art general text embedding
Training state-of-the-art general text embeddingZilliz
 
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxMerck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxLoriGlavin3
 
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024BookNet Canada
 
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your BrandWordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brandgvaughan
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity PlanDatabarracks
 
SALESFORCE EDUCATION CLOUD | FEXLE SERVICES
SALESFORCE EDUCATION CLOUD | FEXLE SERVICESSALESFORCE EDUCATION CLOUD | FEXLE SERVICES
SALESFORCE EDUCATION CLOUD | FEXLE SERVICESmohitsingh558521
 
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxUse of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxLoriGlavin3
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek SchlawackFwdays
 
How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.Curtis Poe
 
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Mark Simos
 
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc
 
DSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningDSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningLars Bell
 
Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Commit University
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 3652toLead Limited
 

Recently uploaded (20)

Gen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfGen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdf
 
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptxThe Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache Maven
 
Generative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersGenerative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information Developers
 
Rise of the Machines: Known As Drones...
Rise of the Machines: Known As Drones...Rise of the Machines: Known As Drones...
Rise of the Machines: Known As Drones...
 
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptxThe Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
 
Training state-of-the-art general text embedding
Training state-of-the-art general text embeddingTraining state-of-the-art general text embedding
Training state-of-the-art general text embedding
 
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxMerck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
 
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
 
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your BrandWordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brand
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity Plan
 
SALESFORCE EDUCATION CLOUD | FEXLE SERVICES
SALESFORCE EDUCATION CLOUD | FEXLE SERVICESSALESFORCE EDUCATION CLOUD | FEXLE SERVICES
SALESFORCE EDUCATION CLOUD | FEXLE SERVICES
 
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxUse of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
 
How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.
 
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
 
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
 
DSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningDSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine Tuning
 
Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365
 

MPEG-DASH Dataset MMSys 2012

  • 1. AC M M u l t i m e d i a Syste m s 2 0 1 2 Dynamic Adaptive Streaming over HTTP (DASH) Dataset Stefan Lederer, Christopher Müller and Christian Feb. 22-24 2012 Timmerer Chapel Hill Alpen-Adria-Universität Klagenfurt (AAU)
  • 2. Motivation • HTTP Streaming has become very popular on the Internet – Various different bitrates of the video are available on the server --> choose the best version to load – Easy to use existing CDN structure – No NAT/Firewall issues due to HTTP – Various technologies • BUT: no standard in use! ACM MMSys 2012, Feb. 22-24, Chapel Hill/NC/USA Slide 2
  • 3. DASH • Dynamic Adaptive Streaming over HTTP (DASH) – Will be Part 6 of MPEG-B – Existing DASH Plugin for VLC ACM MMSys 2012, Feb. 22-24, Chapel Hill/NC/USA Slide 3
  • 4. Dataset • Dataset with DASH Content – Long sequences in high quality – Various segment-length versions – Free available for DASH experiments – PSNR values per frame • Problem: Content Rights – CC-Attribution 2.0 Generic (CC-BY 2.0) License or similar – Free to Share, Free to Remix – Note: YouTube introduces CC-BY in June 2011! • Negotiation with content owner ACM MMSys 2012, Feb. 22-24, Chapel Hill/NC/USA Slide 4
  • 5. Dataset Sequences Name Source Quality Length Genre Big Buck Bunny 1080p YUV 09:46 Animation Elephants Dream 1080p YUV 10:54 Animation Red Bull Playstreets 1080p, 6 Mbit H.264 01:37:28 Sport The Swiss Account 1080p, 6 Mbit H.264 57:34 Sport Valkaama 1080p, 6 Mbit H.264 01:33:05 Movie Of Forest and Men SD 10:53 Movie ACM MMSys 2012, Feb. 22-24, Chapel Hill/NC/USA Slide 5
  • 6. DASH Dataset Sequences ACM MMSys 2012, Feb. 22-24, Chapel Hill/NC/USA Slide 6
  • 7. Bitrates and Resolutions # Animation Sport Movie 1 50 kbit/s, 320x240 100 kbit/s, 320x240 50 kbit/s, 320x240 2 100 kbit/s, 320x240 150 kbit/s, 320x240 100 kbit/s, 320x240 3 150 kbit/s, 320x240 200 kbit/s, 480x360 150 kbit/s, 320x240 4 200 kbit/s, 480x360 250 kbit/s, 480x360 200 kbit/s, 480x360 5 250 kbit/s, 480x360 300 kbit/s, 480x360 250 kbit/s, 480x360 6 300 kbit/s, 480x360 400 kbit/s, 480x360 300 kbit/s, 480x360 7 400 kbit/s, 480x360 500 kbit/s, 854x480 400 kbit/s, 480x360 8 500 kbit/s, 480x360 700 kbit/s, 854x480 500 kbit/s, 854x480 9 600 kbit/s, 854x480 900 kbit/s, 854x480 600 kbit/s, 854x480 10 700 kbit/s, 854x480 1,2 Mbit/s, 854x480 700 kbit/s, 854x480 11 900 kbit/s,1280x720 1,5 Mbit/s,1280x720 900 kbit/s,1280x720 12 1,2 Mbit/s,1280x720 2,0 Mbit/s,1280x720 1,2 Mbit/s,1280x720 13 1,5 Mbit/s,1280x720 2,5 Mbit/s,1280x720 1,5 Mbit/s,1280x720 14 2,0 Mbit/s,1280x720 3,0 Mbit/s,1920x1080 2,0 Mbit/s,1920x1080 15 2,5 Mbit/s,1920x1080 4,0 Mbit/s,1920x1080 2,5 Mbit/s,1920x1080 16 3,0 Mbit/s,1920x1080 5,0 Mbit/s,1920x1080 3,0 Mbit/s,1920x1080 17 4,0 Mbit/s,1920x1080 6,0 Mbit/s,1920x1080 4,0 Mbit/s,1920x1080 18 5,0 Mbit/s,1920x1080 5,0 Mbit/s,1920x1080 19 6,0 Mbit/s,1920x1080 6,0 Mbit/s,1920x1080 20 8,0 Mbit/s,1920x1080 ACM MMSys 2012, Feb. 22-24, Chapel Hill/NC/USA Slide 7
  • 8. DASH Content Types • Segment Size: – Seconds: 1, 2, 4, 6, 10, 15 • File Organization – Segmented – One file per representation, Byte Range Requests • e.g.: Big Buck Bunny – 120 Encodings needed – Only 6 DASH Encoder runs ACM MMSys 2012, Feb. 22-24, Chapel Hill/NC/USA Slide 8
  • 9. DASHEncoder • DASH Content Generation Tool – Encoding + Multiplexing + MPD generation – Generates isoffmain profile compliant MPDs – Fully configurable using a config-file – Enables batch processing – Currently uses x264 and GPAC‘s MP4Box – Easy extensible to further encoders & multiplexers ACM MMSys 2012, Feb. 22-24, Chapel Hill/NC/USA Slide 9
  • 10. DASH Encoder • h.264: x264 / ffmpeg • AAC: ffmpeg Encode • [WebM, etc.] • MP4Box: Video / Audio / Video + Audio • [e.g. WebM/MKV Segmenter] Container • Generate one MPD • Subfolder Organization MPD • MPD Transformation ACM MMSys 2012, Feb. 22-24, Chapel Hill/NC/USA Slide 10
  • 11. Connection Type Evaluation • Peristent vs. non-persisten connections – Bandwidth variations in high delay network – Influence of segment length to streaming performance – Apache Web Server + DASH VLC Plugin (AAU) ACM MMSys 2012, Feb. 22-24, Chapel Hill/NC/USA Slide 11
  • 12. Non-/Persistent Conn. Segment Length: 2 sec. ACM MMSys 2012, Feb. 22-24, Chapel Hill/NC/USA Slide 12
  • 13. Non-/Persistent Conn. Segment Length: 15 sec. ACM MMSys 2012, Feb. 22-24, Chapel Hill/NC/USA Slide 13
  • 14. Evaluation Results ACM MMSys 2012, Feb. 22-24, Chapel Hill/NC/USA Slide 14
  • 15. Conclusion • Public available and free DASH dataset • Common basis for evaluations – DASH Implementations, Stream Switching Algorithms, Network and Cache Configurations, ... – Enables objective comparison of research results • Basic content generation tool: DASHEncoder • Fully compatible to DASH VLC Plugin of ITEC/AAU • Evaluation showing influence of segment length ACM MMSys 2012, Feb. 22-24, Chapel Hill/NC/USA Slide 15
  • 16. Future Work • Dataset – Further profiles and MPDs – Further media segment formats • Evaluation of mobile scenarios – Under vehicular & pedestrian mobility • Peer-assisted DASH – Reduce server bandwidth requirements – Inter-peer synchronisation & communication ACM MMSys 2012, Feb. 22-24, Chapel Hill/NC/USA Slide 16
  • 17. Stefan Lederer Christopher Müller Christian Timmerer Thank You! http://dash.itec.aau.at