Video capacity of WLANs with a multiuser perceptual quality constraint Authors :- Jing Hu, Sayantan Choudhury and Jerry D. Gibson Presenters: Aditya Vashist Bharat Bansal

Overview Introduction Experimental Setup Results of Simulation Proposed Measures : PSNR(r,f) and MOS(r) Video Capacity of WLAN with DCF Quality Constrained Video Capacity and its Application Conclusions Q & A

Introduction Popularity of Wireless LANs Multimedia large part of WLAN transmission how many users can be supported? Video Quality Focus on Video capacity of WLAN maximum number of users Balance between Capacity and quality in the situation when multiple users are using the same network, which is inevitably the case when video capacity is under investigation, the assessment of the quality of multiple videos delivered over the network has not been studied a) designing the network matched to the special characteristics of video b) compressing and transporting video adaptively with respect to the lower layers in the OSI stack of the network, and c) solving the cross-layer design problem as an optimization problem with the objective of selecting a joint strategy across multiple OSI layers. study independent of other parameters like Rate control (RC) and rate distortion optimization (RDO)

Measurement Techniques Measuring video Quality mean squared error, PSNR poor correlation to perceived quality human vision system(HVS) : computationally intensive New Parameter proposed by this paper to measure quality of video over WLAN communication system design However, video quality measurement is a very difficult problem on its own. The conventional measures such as the mean squared error (MSE), or equivalently the peak signal-to-noise ratio (PSNR) of the distorted videos, are often criticized for correlating poorly to perceptual video quality. On the other hand, the objective perceptual video quality measures that are based on the lower order processing of human vision systems (HVS) are computationally very intensive. Thus there is a need for new metric system to analyse the quality which is less expensive and more reliable PSN is statistical video quality indicator and perceptual video quality indicator MOS(mean opinion score)

Experimental Setup Video Codec H.264 (packetized coding scheme with multiple coding schemes/options). GOPs(Group of picture sizes) of 10, 15, 30, 45. 3 videos of 90 frames each. Video codec H.264 which uses packetized coding scheme and is compatible with multiple coding schemes and options IBBBBPBBBBPBBBBI GOP length is 15 that is basically the difference between successive I frames so that to visualize the new frame decoder don’t need the previous frame. 3 video sequences of length 90 were taken for the experiment, selection was based on the criteria that one where two hosts are talking doesn’t have much of the movement of people but the background moves a bot, silent paris stefan

Experimental Setup Diagram

Experimental Setup continues... WLAN: IEEE 802.11a (5GHz; 54 Mbps) Quantization Parameters : 26 (fine) and 30 (coarse) payload size of 100 and 1100 bytes Additive white gaussian noise - AWGN and fading channels I frame packet loss compensation through spatial interpolation P frame packet loss compensation through reference frame Lost frame compensation through copy of previous frame Measurements SNR(signal to noise ratio), PER(Packet Error Rate), Data rates(DR) AWGN and fading channels -> awgn have less attenuation as compared to fading channel Furthermore, the methodology employed is perfectly general and can be used for different networks, video codecs, transmission channels, protocols, and perceptual quality measures. We consider one-hop WLANs, in which case we limit our attention to the PHY, MAC and APP layers.

Results Packet Loss and Video Quality Coded Video Data Rate

Packet loss and video quality cdf of packet error rate <=x same channel SNR and Packet size It shows that for the same channel SNR and the same PS, the PER of an individual multipath fading channel realization can range from 0 to 1, since the cdf’s of the PER of all fading channels are greater than 0 at PER =0 bigger packet size have more chances of losing data over the transmission as compared to 100 bytes AWGN channel is more reliable

Packet loss and video quality •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 channel performance •Variation of PER of AWGN channel is less, ranges from 1% to 3% •Avg. PER of multipath channels = 5.5% è This represents only a small number of total realizations •Avg. PER of AWGN channel is much lower than Avg. PER of channels with fading Average PER over realizations of multipath fading not a reliable measure of video quality as it varies a lot for the same channel and the average

Packet loss and video quality •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

Packet loss and video quality •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

Coded Video Data Rate (1) Video capacity depends on coded video rate. Coded video rate depends on the properties of individual videos and the parameters chosen for the video codec used. Compressed I Frame and P Frame sizes are critical in the video capacity calculation. Coded I Frame size -> Complexity of scene. Coded P Frame size -> Motion across the frames. . In order to study the number of video users i.e. the video capacity . Parameters such as QP , PS, GOP.

Coded Video Data Rate (2)

Coded Video Data Rate (3) GOPS decides the I frame refresh rate . In case of intercoded frames , the coded frame size is unaffected by the distance of the previous intra coded frame.

Proposed Measures : PSNR (r,f) and MOS(r) - (1) Defined as the PSNR achieved by f% of the frames in each one of the r% realizations. f% : captures the majority of the frame. r%: captures the reliability of the channel over many users(0%-100%). Proposal based on following observations recognized by researchers in video quality assessment : Poor quality frames dominates the viewers experience. Quality drop due to few number of poor quality frames are not perceivable by the viewers. If PSNR> Threshold, increasing PSNR does not correspond to an increase in perceptual quality.

Proposed Measures : PSNR (r,f) and MOS(r) - (2) Experiment to confirm observations : Stimulus comparison method used. Two videos of same content played side by side. Left -> Perfect quality video. Right -> Reconstructed video. 3 humans rate the video (opinion score: 0%-100%) 50 video pairs tested. For each video, PSNRs for each frame are calculated. Avg. PSNR and PSNR(f) are also calculated. I.e. to confirm the correlation between the perceived video quality and the psnr(f) and to find the suitable value of ‘f’. Asked to rate the video on gthe right as compared to the video on the left.

Proposed Measures : PSNR (r,f) and MOS(r) - (3) Experiment (cont...) Average PSNR and PSNR(f) are mapped to the opinion scores. f -> 0.5-0.99 Mean opinion score achieved by r% of the transmissions(MOS(r)) is given by : PSNR(f) with f=90% correlates to the opinion score best for medium frame rate. Shortcomings of average PSNR: Underestimates the quality at high quality level. Overestimates the quality at low quality level.

Proposed Measures : PSNR (r,f) and MOS(r) - (4)

Proposed Measures : PSNR (r,f) and MOS(r) - (5) PSNR(r,f)/MOS(r) -> new multiuser perceptual quality indicator . PSNR(r,f) -> distribution of the video quality across the frames and channel MOS(r) -> guidance on the perceptual quality across different users Indicator is independent of simulation setup.

Video Capacity of WLAN with DCF(1) DCF is based on CSMA/CA. Thumb Rule : Video frames must arrive at the playout buffer before their respective playout deadlines. Divide the transmission deadline by the transmission time to calculate the number of users that can be supported. Assumptions for video capacity calculation : All the users are communicating the same video type. H.264/AVC is the default video codec. Similar value of the video codec parameters are chosen for all users.

Video Capacity of WLAN with DCF(2) Requirement to know the number of users supported: Network operator can get an idea of number of users that can be supported for identical traffic. Mix of users having different traffic demands -> capacity is approximated to an interpolation of capacity values for each traffic category.

Quality Constrained Video Capacity and its Applications(1)

Quality Constrained Video Capacity and its Applications(2)

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: System Performance Evaluation Accurate System Design Link adaptation based on capacity

Conclusion(1) Average PER/ Average PSNR -> Not a suitable indicator of video quality. They should not serve as a basis for video quality assessment. Proposed perceptual quality indicator matches the quality with the human vision systems quality perception. Quality indicator plus video capacity -> design better WLAN communication system with importance to both quality and efficient capacity utilization .

Conclusion(2) Some important observations: Video quality perception is a highly subjective test. Ideal test conditions are assumed . 802.11a has been used for testing;however, today 802.11g/n is widely used.Multipath fading parameters may change due to operating frequency in 802.11g/n(2.4GHz). Portability of the proposed method have been assumed but not proved.

Q & A

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