Evaluating Quality of Experience in IPTVServices Using MPEG Frame Loss RateDiego Hernando Loeda1, Jorge E. López de Vergar...
Introduction ISPs and network operators providingmultimedia services QoS vs QoE Evaluation types (FR, RR, NR) MPEG fr...
Related WorkOriginalVideoDistortedVideoFRRRInformationextractionNREvaluating Quality of Experience in IPTV ServicesUsing M...
Method DescriptionBroadcast Replay VQMPLRMPEG LossGilbert-Elliot Modelfor packet lossesG Brp1-r1-pEvaluating Quality of Ex...
QoE Prediction Model (PLR) Based on Packet Loss Ratio Average of Point CloudsMOS(PLR)=a+b·PLR 9.4% error in MOS scale ...
QoE Prediction Model (IBP) Types of frames in MPEG» Intra-coded Frame» Predictive Frame» Bi-Predictive FrameEvaluating Qu...
QoE Prediction Model (IBP) Initial IBP Loss Model Based on loss ratio ofeach type of frameMOS(I,B,P)=a+b·I+c·B+d·P 9.5%...
QoE Prediction Model (IBP) Importance of frameheaders in packet lossesEvaluating Quality of Experience in IPTV ServicesUs...
QoE Prediction Model (IBP) Final IBP Loss Model Based on loss ratio of eachtype of frame… …But also taking into account...
EfficiencyLow Quality Videos (CIF, QCIF) ~400 channels 10s captures can beanalyzed per second using the IBPmodel Linear ...
Conclusions & Future Work Conclusions» We have obtained a new NR QoE estimation method– Based on MPEG traffic characteris...
Evaluating Quality of Experience in IPTVServices Using MPEG Frame Loss RateDiego Hernando Loeda1, Jorge E. López de Vergar...
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Evaluating Quality of Experience in IPTV Services Using MPEG Frame Loss Rate

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Current network services such as Voice over IP or IP Television pose new challenges to network providers. Network operators need to know if their services are being properly provided. However, current quality of service parameters commonly used in data networks (e.g. throughput, packet delays, packet losses, etc.) do not show a clear view of what the users are experiencing. Thus, it is necessary to translate such measured quality parameters into a quality of experience value. Several models are being developed to cope with this problem. For instance, some approaches have used the packet loss rate to evaluate the experienced quality of an IP television channel. Unfortunately, packet loss just explains a fraction of the quality behavior. Then, we go one step further, taking into account the different MPEG frame types that are transmitted. In this paper, we have defined a model to predict the experienced quality that is a function of the loss of the different types of MPEG frames, providing a mean opinion score of the delivered service. The final results show that our model is able to better predict the quality of experience of such video services than just using the packet loss rate.

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Evaluating Quality of Experience in IPTV Services Using MPEG Frame Loss Rate

  1. 1. Evaluating Quality of Experience in IPTVServices Using MPEG Frame Loss RateDiego Hernando Loeda1, Jorge E. López de Vergara1,2,David Madrigal1, Felipe Mata 1<jorge@naudit.es>1Universidad Autónoma de Madrid, Spain2Naudit High Performance Computing and Networking, SpainFirst International Workshop on Quality Monitoring,SaCoNeT, 17th June 2013, Paris, France
  2. 2. Introduction ISPs and network operators providingmultimedia services QoS vs QoE Evaluation types (FR, RR, NR) MPEG frame types Resource consumptionEvaluating Quality of Experience in IPTV ServicesUsing MPEG Frame Loss Rate 2
  3. 3. Related WorkOriginalVideoDistortedVideoFRRRInformationextractionNREvaluating Quality of Experience in IPTV ServicesUsing MPEG Frame Loss Rate 3
  4. 4. Method DescriptionBroadcast Replay VQMPLRMPEG LossGilbert-Elliot Modelfor packet lossesG Brp1-r1-pEvaluating Quality of Experience in IPTV ServicesUsing MPEG Frame Loss Rate 4
  5. 5. QoE Prediction Model (PLR) Based on Packet Loss Ratio Average of Point CloudsMOS(PLR)=a+b·PLR 9.4% error in MOS scale R² = 0.6974Variable Coeff Stdr. Error t value p-valuea 4.9442 0.0165 299.4776 0b -0.1642 0.0034 49.6947 0SEE 0.3757R² 0.69745
  6. 6. QoE Prediction Model (IBP) Types of frames in MPEG» Intra-coded Frame» Predictive Frame» Bi-Predictive FrameEvaluating Quality of Experience in IPTV ServicesUsing MPEG Frame Loss Rate 6
  7. 7. QoE Prediction Model (IBP) Initial IBP Loss Model Based on loss ratio ofeach type of frameMOS(I,B,P)=a+b·I+c·B+d·P 9.5% error in MOS scale R² = 0.6969Variable Coeff Stdr. Error t value p-valuea 4.9437 0.0165 298.7312 0b -17.5488 1.5542 11.2913 0c -0.8657 0.3376 2.4212 0.0078d 1.6701 0.9601 1.6738 0.0472SEE 0.3764R² 0.6969 7
  8. 8. QoE Prediction Model (IBP) Importance of frameheaders in packet lossesEvaluating Quality of Experience in IPTV ServicesUsing MPEG Frame Loss Rate 8Without header analysis  With header analysis
  9. 9. QoE Prediction Model (IBP) Final IBP Loss Model Based on loss ratio of eachtype of frame… …But also taking into accountthat a header loss implieslosing the whole frameMOS(I,B,P)=a+b·I+c·B+d·P 8.4% error in MOS scale R² = 0.7575Variable Coeff Stdr. Error t value p-valuea 4.9030 0.0142 344.9143 0b -1.0823 0.2935 3.6876 0.0001c -3.2792 0.4759 6.8911 0d -3.2323 0.2784 11.6111 0SEE 0.3367R² 0.7575 9
  10. 10. EfficiencyLow Quality Videos (CIF, QCIF) ~400 channels 10s captures can beanalyzed per second using the IBPmodel Linear growthHigh Quality Videos (1920x1080) ~60 channels 10s captures can beanalyzed per second using the IBPmodel Linear growthEvaluating Quality of Experience in IPTV ServicesUsing MPEG Frame Loss Rate 10
  11. 11. Conclusions & Future Work Conclusions» We have obtained a new NR QoE estimation method– Based on MPEG traffic characteristics– No video signal is decoded» Reasonable computational cost– Deployable for current network services Future Work» Investigate about the amount of movement» Create a video database with different qualities and GOPstructures» Perform a subjective evaluation for a better validationEvaluating Quality of Experience in IPTV ServicesUsing MPEG Frame Loss Rate 11
  12. 12. Evaluating Quality of Experience in IPTVServices Using MPEG Frame Loss RateDiego Hernando Loeda1, Jorge E. López de Vergara1,2,David Madrigal1, Felipe Mata 1<jorge@naudit.es>1Universidad Autónoma de Madrid, Spain2Naudit High Performance Computing and Networking, SpainFirst International Workshop on Quality Monitoring,SaCoNeT, 17th June 2013, Paris, France

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