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Measuring Quality of Experience for Successful IPTV Deployments

Explore the challenges and solutions in measuring the Quality of Experience (QoE) for IPTV deployments, including traditional measurements (QoS) vs. QoE, possible measurement approaches, and end-to-end QoE management.

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Measuring Quality of Experience for Successful IPTV Deployments

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  1. Measuring Quality of Experience for Successful IPTV Deployments Dr. Stefan Winkler

  2. Outline • Challenges • Digital Video Quality Issues • Traditional Measurements (QoS) vs. Quality of Experience (QoE) • Possible Solutions • QoE Measurement Approaches • End-to-end QoE Management • Conclusions

  3. Digital Video Challenges Demanding traffic profiles • High bandwidth streams • High traffic volumes • Live, VOD Network effects • Video impacted heavily with minor network impairments • Multi-vendor network complicates diagnosis / troubleshooting Service quality degradations Difficult diagnosis, troubleshooting Rising management and OPEX costs Higher customer churn High end-user expectations • Defined with decades of history • Grow rapidly with HD • Low tolerance for poor quality New architectures • Sensitive video processing devices create possibility for various impairment sources • Ad-insertion, middleware

  4. What Drives End-Users Source: MRG 2007 IPTV Video Quality Survey, available at http://qoe.symmetricom.com

  5. Service Providers View Source: MRG 2007 IPTV Video Quality Survey, available at http://qoe.symmetricom.com

  6. Service Providers’ View Source: MRG 2007 IPTV Video Quality Survey, available at http://qoe.symmetricom.com

  7. Sources of Video Issues Consider all elements for true end-to-end solution

  8. Compression Artifacts Original MPEG-2 H.264

  9. PSNR vs. QoE Same amount of distortion (PSNR) – different perceived quality Understand & model human vision system

  10. Quality of Service Network-centric Delay, packet loss, jitter Transmission quality Content agnostic Quality of Experience Content impairments Blockiness, Jerkiness, … End-user quality Application driven QoS vs. QoE QoS QoE

  11. QoS vs. QoE • Same network impairments • Packet Loss: 1% • Delay: 10ms • Jitter: 50us • Bandwidth: 500kbps • Different perceived quality!

  12. MDI vs. QoE • Media Delivery Index (MDI) • MDI consists of two metrics: • Delay Factor (DF) • Media Loss Rate (MLR) • MDI limitations: • MDI assumes constant bit rate (CBR) traffic • MDI does not consider video payload or content • MDI values are not intuitive • MDI doesn’t correlate with video quality

  13. MDI vs. QoE MOS Media Loss

  14. QoS/QoE Cycle Alignment gap Service provider End-user DesiredQoE Targeted QoS Value gap Execution gap Perceived QoE DeliveredQoS Perception gap Adapted from ITU-T Rec. G.1000 and COM12–C185–E

  15. Outline • Challenges • Digital Video Quality Issues • Traditional Measurements (QoS) vs. Quality of Experience (QoE) • Possible Solutions • QoE Measurement Approaches • End-to-end QoE Management • Conclusions

  16. Video Video Compression/TransmissionSystem Full-Reference Approach Sender Receiver • Comparison of individual video frames • Offline analysis (capture is required) – lab applications • High detail and accuracy • Alignment procedure Full Ref. QualityMeasurement Full reference information

  17. Video Video Compression/TransmissionSystem No-Reference Approach Sender Receiver • Non-intrusive, in-service measurement • Real-time monitoring applications • No alignment required No-Ref. QualityMeasurement

  18. Video Video Compression/TransmissionSystem Reduced-Reference Approach Sender Receiver • Monitoring applications • Correlation of content and network impairments • Encrypted environments Feature Extraction Reduced Ref.Measurement Feature Extraction

  19. Content & Network Metrics (Correlation Engine) "Vision is the most highly developed of the human senses, so people are even more sensitive to flaws in video images than, say, the sound of a telephone conversation.”Ken Wirt, Cisco Vice President Consumer Marketing, Jan 2008

  20. Vision Modeling • Contrast perception • Visibility of different patterns • Frequency dependencies • Masking effects • Interaction of content and impairments • Texture, edges, luminance • Spatial and temporal masking • Color perception Sensitivity Temporal frequency [Hz] Spatial frequency [cpd] Visibility threshold Maskingcurve Thresholdwithoutmasker Target contrast Masker contrast

  21. End-to-end QoE • Deep Content Analysis (bitstream) • Detect content impairments • Deep inspection to associate content to timestamps (eg: TS1 = I-Frame) • Deep Content Analysis(pixel by pixel) • Source content and encoder / transcoder validation • Network (header or stream) Analysis • Detect QoS issues • Content analysis where possible (unencrypted) • Inspection of QoS to associate timestamps to impairments (eg: TS1 = PacketLoss) Content Stream Analysis: • PES inspection • PCR jitter etc. Content Impairments: • Blockiness, blur • Jerkiness • Freeze/black frame • Noise, Color TS1 = I-Frame Q-Advisor Network Impairments: • Loss • Delay • Jitter • Bandwidth Correlation Engine TS1 = Packet Loss Packet Loss -> I-Frame Human Vision System Model VideoQualityReports

  22. 5 Imperceptible 4 Perceptible 3 Slightly Annoying 2 Annoying 1 Very Annoying IPTV QoE Management • 1. Understand the Service • Is there an issue? • Does it matter? • 2. Understand the Problem • What does the customer see? • What is the exact cause? 1.0 • 3. Understand the Solution • What is the impairment source?

  23. Conclusions • QoE is application-driven • Measure both network and content impairments • QoE is user-oriented • Measure how end-user perceives service issues • End-to-end quality measurement • Cover different impairment sources • Identify problem causes

  24. Contact Info Stefan Winklerswinkler@symmetricom.com Company:qoe.symmetricom.com Further Reading:stefan.winkler.net/book.html

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