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Video Capacity of WLANs with a Multiuser Perceptual Quality Constraint

Video Capacity of WLANs with a Multiuser Perceptual Quality Constraint. Authors: Jing Hu , Sayantan Choudhury , Jerry D. Gibson Presented by: Vishwas Sathyaprakash, Aditya Sharma. Overview. Introduction & Motivation Experiment – Simulation Setup Results of Simulation

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Video Capacity of WLANs with a Multiuser Perceptual Quality Constraint

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  1. Video Capacity of WLANs with a Multiuser Perceptual Quality Constraint Authors: Jing Hu, SayantanChoudhury, Jerry D. Gibson Presented by: Vishwas Sathyaprakash, Aditya Sharma

  2. Overview • Introduction & Motivation • Experiment – Simulation Setup • Results of Simulation • Packet Loss and Video Quality • Video Data Rate • Suggested Improvements • Determining Video Capacity • Applications • Conclusion

  3. Introduction • Wireless LANs gaining popularity • Multimedia - Large part of WLAN traffic • How many users can be supported ? • Video Quality - as perceived by users • Fine Balance - Capacity v/s Quality • Authors  Define this fine balance in terms of perceived quality of the video being delivered to r% of the users • Example: Streaming a video in this class

  4. Motivation • Video  Compression  Variable Size & Quality • Measuring video quality: • Mean Squared Error & Peak Signal-to-Noise Ratio • Poor co-relation to perceived video quality • HVS : Computationally Expensive • Capacity, Encoding and transfer rates • Capacity calculation not defined clearly • Transfer rate/Capacity depends on codec used • First effort to relate Quality and Capacity: Not been studied yet

  5. Overview • Introduction & Motivation • Experiment – Simulation Setup • Results of Simulation • Packet Loss and Video Quality • Video Data Rate • Suggested Improvements • Determining Video Capacity • Applications • Conclusion

  6. Video Over WLAN: Simulation Setup • Video Codec: H.264 • Packetized Video; Many coding schemes/options • GOPs of 10, 15, 30, 45 • 3 Videos: 90 Frames each • Silent.cif; Paris.cif; Stefan.cif

  7. Video Over WLAN: Simulation Setup • WLAN: IEEE 802.11a (5GHz; 54 Mbps) • Quantization Parameters (QP): 26 (fine) & 30 (coarse) • Payload Size (PS) of 100 and 1100 bytes • Noise: Additive White Gaussian Noise (AWGN) • Packet Loss Compensation: Base Model • I-Frame: Recovery of MB by Spatial Interpolation • P-Frame: Copying MB from reference frames • Lost Frame: Entire frame is copied • Measurements: SNR, PER and Data Rates (DR)

  8. Overview • Introduction & Motivation • Experiment – Simulation Setup • Results of Simulation • Packet Loss and Video Quality • Video Data Rate • Suggested Improvements • Determining Video Capacity • Applications • Conclusion

  9. Results of Simulation: Packet Loss (1) Number of Realizations

  10. Results of Simulation: Packet Loss (2) • CDFs of PER > 0 at 0 realizations • CDFs of PER < 1 for realizations <1 • Average PER over realizations of multipath fading not an appropriate indicator of channe performance • Variation of PER of AWGN channel is less, ranges from 1% to 3% • Avg. PER of multipath channels = 5.5%  This represents only a small number of total realizations • Avg. PER of AWGN channel is much lower than Avg. PER of channels with fading

  11. Results of Simulation: Packet Loss (3) Number of Realizations 90%: Packet Loss < 2% 70%: No Packet Loss

  12. Results of Simulation: Packet Loss (3)…contd. PSNR of each frame • Video: Silent.cif • QP = 26, 30 • GOP = 15 • PS = 100 • Fading Channels • Thick lines represent Average PSNR • ‘+’ marks: 70% of the overlapping realizations with no packet loss Frame Index; QP = 26 PSNR of each frame Frame Index; QP = 30

  13. Results of Simulation: Packet Loss (4) PSNR of each frame • Video: Silent.cif • QP = 26, 30 • GOP = 15 • PS = 100 • Shows behavior of noise channels (AWGN) • Prediction in video encoding causes realizations with similar PER: yet, completely different video quality Frame Index; QP = 26 PSNR of each frame Frame Index; QP = 30

  14. Results of Simulation: Data Rate (1)

  15. Results of Simulation: Data Rate (2)

  16. Overview • Introduction & Motivation • Experiment – Simulation Setup • Results of Simulation • Packet Loss and Video Quality • Video Data Rate • Suggested Improvements • Determining Video Capacity • Applications • Conclusion

  17. PSNRr,f& Perceptual Quality of Multiple Users MOSr • Defined as the PSNR achieved by f% of the frames in each one of the r% realizations • f%: Captures the majority of the frames • r%: Captures the reliability of a channel over many users • Claim: Quality Perception doesn’t change for high ‘f’ • Observations behind the claim: • Poor quality frames dominate viewers’ experience • Quality drop in a very small number of frames is not perceivable by the human viewer • PSNR > threshold  Increase in PSNR doesn’t translate to increase in perceptual quality

  18. PSNRf and Perceived Video Quality (1) • Experiment to prove the claim: • Same video sequences played side-by-side • Left: Raw video / Perfect Quality • Right: Compressed Video with recovered packet losses and concealment • 3 humans: Rate the videos from 0 – 100% • Scores plotted

  19. PSNRf and Perceived Video Quality (2)

  20. PSNRf and Perceived Video Quality (3) • Of all f values, f=90% correlates to best opinion score • Mean Opinion Score achieved by r% of the transmissions is given by: • Dotted Lines = Average PSNR (existing) • Problem with regular PSNR: • Quantitative measure of Quality • Underestimates the quality at high quality levels • Overestimates the quality at low quality levels • Thick lines = PSNRf (proposed) • Serves as effective quality measure: correctly estimates low quality and high quality as perceived by HVS

  21. Overview • Introduction & Motivation • Experiment – Simulation Setup • Results of Simulation • Packet Loss and Video Quality • Video Data Rate • Suggested Improvements • Determining Video Capacity • Applications • Conclusion

  22. Video Capacity of WLAN with DCF (1) • Thumb-rule: Video frames must arrive at the buffers before the playback deadline. • DCF: Distributed Coordination Function: based on CSMA/CA • Requirement to know the number of users supported: • Network operators get an idea of number of users that can be supported for identical traffic (capacity planning) • Mix of users having different traffic demands  capacity is approximated to an interpolation of capacity values for each traffic category

  23. Video Capacity of WLAN with DCF (2) • Video capacity with no buffer at receiver • I-Frame and P-Frame sizes differ greatly • What happens when: • All users are transmitting I-Frame? - Worst Case • All users coordinate I-Frame transmission ? - Best Case

  24. Video Capacity of WLAN with DCF (2) • Video capacity with play-out buffer at receiver (Cb) • Play-out buffer b is the length of the buffer (ms) • It is used only for the frames that have more bits than the other frames (I-Frame) • How buffer length compares with SI/SP : Plot • Cb fluctuates between CM and a lower bound given by:

  25. Video Capacity of WLAN with DCF (2) • Length of buffer required for video capacity to reach upper bound, for typical SI/SP values:

  26. Overview • Introduction & Motivation • Experiment – Simulation Setup • Results of Simulation • Packet Loss and Video Quality • Video Data Rate • Suggested Improvements • Determining Video Capacity • Applications • Conclusion

  27. Quality Constrained Video Capacity and its Applications (1)

  28. Quality Constrained Video Capacity and its Applications (2)

  29. Quality Constrained Video Capacity and its Applications (3) • Observations: • Users watching silent.cif  Excellent Quality • Users watching paris.cif  Average Quality • Users watching stefan.cif  Poor Quality • Applications: • Link Adaptation based on capacity • System performance evaluation • Accurate System Design

  30. Overview • Introduction & Motivation • Experiment – Simulation Setup • Results of Simulation • Packet Loss and Video Quality • Video Data Rate • Suggested Improvements • Determining Video Capacity • Applications • Conclusion

  31. Conclusion • Average PER / Average PSNR  Not a suitable indicator of video quality • They should not serve as the basis for video quality assessment • Proposed ‘perceptual’ quality indicator matches the quality with the human vision system’s quality perception • Video Capacity with/without buffering • Quality Indicator + Video Capacity  design better WLAN communication system with importance to both quality and efficient capacity utilization

  32. Conclusion (2) • Some observations: • Video Quality Perception is a highly subjective test • Only 3 human ‘testers’ considered: Results could vary with more humans testing the theory • Ideal test conditions assumed: • Packets/Frames received without errors • Collisions are not considered • Single Hop networks considered. More Packet errors / losses in multi-hop networks: Losses affect video quality directly • 802.11a has been used for testing; Widespread use of 802.11g/n today: Multipath Fading parameters may change due to operating frequency in 802.11g/n (2.4GHz) + Interference • Little description of actual applications of the proposed method • Portability of proposed method assumed: Same results expected in other video coding methods (MPEG-2) but not proved

  33. Q & A Note: All images are the property of the respective owners. Images used for non-profit / educational purposes only.

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