An Approach to Addressing QoE for Effective Video Streaming

Transcription

1 Pause Intensity An Approach to Addressing QoE for Effective Video Streaming Xiaohong Peng Electronic, Electrical and Power Engineering School of Engineering & Applied Science Aston University Birmingham, UK 1

2 Facts Globally, Internet video traffic will be 55 percent of all consumer Internet traffic in 2016 (not including video exchanged through peer-to-peer (P2P) file sharing). The sum of all forms of video (TV, video on demand [VoD], Internet, and P2P) will be approximately 86 percent of global consumer traffic by )Cisco White Paper, 2012( 2

3 Mobile Video Traffic (PB per month) Data volume (Petabyte) Annual growth rate: 90% Mobile video --- All video that travels over a 2G, 3G, or 4G network. )Cisco White Paper, 2012( 3

4 Challenge QoE vs resource cost: To address the requirement of end user s perceived quality with limited network resources. QoE Network QoS QoE QoE. End user 4

5 Background Streaming Architecture 5

6 Video Transmission Impairments For UDP/IP systems: blurriness blockiness sharpness or a combination of related artifacts 6

7 Video Transmission Impairments For TCP/IP systems: discontinuity 7

8 Quality Measurement For UDP/IP systems: PSNR (peak signal-to-noise ratio) Server Network Client comparison PSNR 8

9 For TCP/IP systems: PSNR won t work. Quality Measurement Pause frequency doesn t correlate with subjective opinions. Playout Server Network buffer Client Pause Freq: 0.03 Pause Freq: 0.25 pause start pause start Time (s) 9

10 MOS Pause Frequency & MOS Video ID 0 Video ID 1 Video ID 3 Video ID 2 Video ID 4 Video ID 6 Video ID 5 Video ID 7 Video ID 9 Video ID 8 Video ID 12 Video ID 13 Video ID 10 Video ID 11 Video ID 14 Video ID Pause Frequency 10

11 Is there an objective metric that can be closely correlated with subjective assessment (QoE)? 11

12 Buffer Architecture 12

13 Data In Buffer The Playout Buffer Throughtput = Rate Time 13

14 Buffer Occupancy (pkt) The Playout Buffer average > average = average < time(sec) 14

15 A Pause-Play Period Number of buffered packets q max q min t v1 v t max w v t v2 time 15

16 Pause Frequency f = 1/w = η(λ η)/αλ η --- network throughput λ --- playout rate α --- fluctuation range of the buffer (q max q min ) 16

17 Average Pause Duration (Length) v = α/η Average pause duration is independent of the video playout rate. 17

18 Pause Intensity PI = f x v = 1 η/λ PI is determined by traffic property (η ) and service grade (λ). PI represents the relative effectiveness of throughput compared to the required playout rate λ PI is a no-reference and objective metric. 18

19 Throughput ( ) Rate of change of w ( ) Critical points of pause-play sequence in relation to throughput and packet loss probability 136 A = 0 Rate of change of w ( ) Throughput ( ) B = 0 / > 0 P 0 0 < 0 C Probability of packet loss (p) P 1 19

20 pause duration (sec) pause Frequency pause intensity Simulation Results Buffer Behaviour Metrics vs Probability of Packet Loss Simulation 2 Model Fitted Curve (a) Probability of packet loss (p) 0.05 Simulation Model Fitted Curve (b) Probability of packet loss (p) Simulation Model Fitted Curve (c) Probability of packet loss (p) 20

21 Subjective Test: MOS vs Buffer Behaviour Metrics Video ID Video content PI Pause Frequency Average Pause Duration (sec) MOS 0 M M M M M M M M M M M M M M M M R R R R R R R R R R N N N N N N N N N N C C C C C C C C C C

22 MOS MOS MOS Subjective Test: MOS vs Buffer Behaviour Metrics M R1 N C M R1 N C M R1 N C (a) Pause Intensity (PI) (b) Pause Frequency (c) Pause Duration (sec) 22

23 Subjective Test (extreme cases) Video ID PI Pause Freq Avg. Pause Duration MOS

24 MOS MOS MOS Subjective Test (extreme cases): MOS vs Buffer Behaviour Metrics r = r = r = (a) Pause Intensity (PI) (b) Pause Frequency (c) Pause Duration (sec) 24

25 Pearson Correlation Coefficient (r) Pause Frequency Pause Duration Pause Intensity Subjective Testing -1 Moto GP (M) Run (R1) News (N) Cartoon (C) Subjective Testing -2 Rally (R2)

26 PSNR PSNR vs Pause Intensity Quality ID9 Quality ID 10 Quality ID 11 Quality ID 7 Quality ID 8 Quality ID 1 QUality ID 3 Quality ID 2 Quality ID 4 Quality ID5 Quality ID 6 Quality ID 0 Quality ID 12 Quality ID Quality ID 14 Quality ID 15 Quality ID 16 Quality ID Quality ID 18 Quality ID Pause Intensity 26

27 PI-Driven Adaptive Streaming Video server.. Video server λ PI Gateway η.. λ η' QoE 27

28 Optimisation Strategy λ PI PI min Scheduling Optimisation algorithm Link adaptation λ HARQ QoE 28

29 Capacity offered per user (Mb/s) Result: Performance LTE Scheduling/MCS performance vs SNR, VehA, 1.4MHz, nue=10 Round Robin maxci PI l, PI h = 0.2, 0.5 PI l, PI h = 0.2, 0.7 PI l, PI h = 0.2, SNR [db] 29

30 Jane fairness index Result: Fairness Jane index over the accumulated allocated capacity, VehA, 1.4MHz, nue= Round Robin maxci PI l, PI h = 0.2, 0.5 PI l, PI h = 0.2, 0.7 PI l, PI h = 0.2, time (TTI) 30

31 Optimisation Strategy (Cont.) Trade-off between two quality aspects: Continuality PI Resolution (image quality) PSNR (related to λ) The optimisation is to find best trade-off pair (PI, PSNR) for the given available resources such as bandwidth, so that the maximum QoE can be achieved. 31

32 PSNR Pause Intensity Trade-off Between PSNR and PI PSNR PI Packet Loss Rate (%)

33 Pause Intensity shows a strong correlation with video quality perceived by the viewer. Pause Intensity can be used to adaptively regulate video traffic to meet QoE requirements with network resource constraint. Summary Recent publication: Model and Performance of a No-Reference Quality Assessment Metric for Video Streaming to appear in IEEE Trans. on Circuits and Systems for Video Technology. 33

34 Thank You Q&A 34