Providing Smoother Quality Layered Video Stream Shirhari Nelakuditi Raja R Harinath Ewa Kusmierek Zhi-Li Zhang Proceedings of NOSSDAV 2000

Outline Introduction Smoothness Criteria and Quality Metrics Optimal Layer Selection –Maximize Average Run Length –Maximize Minimal Run Length –Maximize Expected Run Length Experimental Result Conclusion

Introduction Streaming Constraints –Network Bandwidth –Client Buffer Solution –Non-Layered Video : A video with different quality (bit rate) version. –Layered Video : A video is split into layers, and provide finer control on video quality.

Introduction Problem –Difficult to select layers such that better but consistent quality is ensured when the network condition are constantly varying. Object –Address the layer selection problem in layered video delivery and show how smoother quality video playback can be provided by utilizing the client buffer for prefetching.

Smoothness Criteria and Quality Metrics How to define a metric that captures the user’s perception of video quality –The higher the amount of detail in the video, the better is its quality. –It is visually more pleasing to watch a video with consistent, albeit lower, quality than one with highly varying quality

More gradual Fewer change

Smoothness Criteria Metrics of the quality smoothness criteria –Higher weight to lower layers –Longer run (A sequence of consecutive frames shown in a layer) M = (m 1, m 2, …, m L ), L : number of layer M 1 is smoother than M 2 if exist i that m 1 j = m 2 j, j m 2 i Example: M 1 = (1, 1, 2, …) M 2 = (1, 1, 1,…..)

Quality Metric /N/N /N/N k : the total number of urns in a layer n 1,,,n k : the lengths of these runs N : the length of the video sequence

Exprun : (( )/4)/12 = Minrun : 1/12 = Exprun : ( )/12 = 0.104

Problem Formulation Time Slot : represents the unit of time for playing back a video frame Client buffer underflow curve with respect to S Client buffer overflow curve with respect to S

Problem Formulation A(S) is said to be feasible with respect to S if and only if for i = 0, 1,,,,,N 1.Rate constraint : i.e. a i (S) <= C i 2.Buffer constraint : A i (S) <= U i (S) 3.Playback constraint : D i (S) <= A i (S)

Optimal Layer Selection Assumption –Each layer in the video is of CBR. –All layers are the same bit rate. –Size of each frame in a layer is 1. –Buffer and bandwidth values are all integers. //From lower layer to higher layer Residual Bandwidth Residual Buffer Optimal subset

Maximize Average Run Length Key : minimizing the number of runs while keeping the sum of all the runs as high as possible. A new run is not initiated unless the buffer is accumulated

Forward Scan Backward Scan

Forward Scan Buffer = 3 (1) Buffer = 1 S 1 = 0 (2) Buffer = 1 S 2 = 0 (3) Buffer = 2 S 3 = 0 (4) Buffer = 3 S 4 = 0 (5) Buffer = 3 S 5 = 1 (6) Buffer = 2 S 6 = 1 (7) Buffer = 2 S 7 = 1 (8) Buffer = 1 S 8 = 1 (9) Buffer = 0 S 9 = 1 (10) Buffer = 0 S 10 = 0

Backward Scan

Maximal Average Run Length Forward Scan –Identify the end of each run. –Minimal number of runs. Backward Scan –Extend each run towards the front of it. –Maximizing the residual buffer made available to higher layers. –Calculate residual bandwidth and buffer for higher layers.

Maximize Minimum Run Length Key –Reduce the variance among the runs. –Grow the shorter runs while shrinking their longer neighbor runs. Step 1.Start with a MAX_AVG_RUN 2.Readjust the length of each run without increasing the number of runs

Maximize Minimum Run Length n k and n k+1 are run length of consecutive run. x k+1 is the limit length that run k+1 can grow. if n k <= n k+1 continue with the next pair else select min((n k -n k+1 +1)/2, x k+1 ) number of frames

Maximize Expected Run Length Key –The longer runs are in the sequence, the higher it’s expected run length is. –Extension of a longer run contributes more towards the expected run length than that of a shorter run.

Experiment Result The video consists of 4 layers. Mean Bandwidth : 0<frame<10000 : <frame<20000 : <frame<30000 : 4.5 Varying bandwidthBuffer of 30

Buffer of 300 Buffer of 900

Conclusion We defined smoothness criteria and designed metrics namely, avgrun, minrun, and exprun for measuring smoothness We developed an optimal offline algorithm to find maximal average run length when network conditions are known a priori