1. An Analytical Model for Progressive Mesh Streaming Wei Cheng, Wei Tsang Ooi School of Computing, National University of Singapore. Sebastian Mondet, Romulus Grigoras, Geraldine Morin, IRIT/ENSEEIHT, France.

2. 2 Outline  Background and motivation of our research  An analytical model for progressive mesh streaming  The main insight from the model  A sending strategy based on our model

3. 3 Applications of 3D Streaming  Virtual Museums e.g. UC Davis Geology Department

4. 4 Applications of 3D Streaming  Virtual Reality / Games: Second Life Active Worlds

5. 5 Huge Amount of Data 4.9 MB 14 MB 155 MB 2 GB Models from http://www-graphics.stanford.edu/

6. 6 Progressive Streaming

7. 7 A series of edge collapses A series of vertex splits Progressive Mesh (Hoppe ‘96)  Based on edge collapse

8. 8 Base mesh vs1vs2vs3vs4vs5vs6vs7vs8 Progressive Streaming  Base mesh + a series of vertex splits

9. 9  Vertex to be split should exist.  The four neighbor faces should exist to avoid illegal split. V V1 V2 V3 V4 V5 V1V2V3V4V5 V Dependency Among Vertex Splits

10. 10  Directed acyclic graph (DAG) directed acyclic graph Vertex splitdependency Representation of Dependency

11. What is the Research Question?

12. 12 The Research Question  Effect of dependency on video streaming is well known.  What is the effect of vertex split dependencies on progressive mesh streaming?

13. 13  Longer chain of dependencies than in video. I P P P Progressive Mesh MPEG1 B B B Property 1 I P P P B B B

14. 14 Retransmission is Needed  One packet loss may disable the decoding of many subsequent vertex splits.  Retransmission is important.

15. 15 Importance of a Vertex Split  The increase in mesh quality after decoding this vertex split.  Any quality metric can be used in our model, e.g. Hausdorff distance View dependent metrics

16. 16 importance Vertex splits Property 2  The importance of vertex splits decreases quickly.

17. 17 Retransmission Has Higher Priority  When we need to choose between retransmission and sending new data, it is better to retransmit lost packet.  Because the older data is typically more important.

18. packet 1 is lost packet 1 is retransmitted time quality Case 2 Case 1 Case1: all following packets dependent on the lost packet Case2: all following packets are independent.

19. 19 time quality Quality Curve  Objective is to improve the quality on the client side.  The quality changes with time.

20. 20 time quality Our Objective  Analytically estimate the cumulative quality of the decoded mesh at a given time t (area under the curve).

21. 21 quality time0 t DvDv wvwv Decoded Mesh Quality  Area under the curve

22. 22 The Key is D v  D v is a random variable since packet loss is random.  Need to find E[D v ] for each vertex split.  D v depends on Loss rate (channel property) Dependencies among data (data property)

23. 23 Outline  Background and motivation of our research  An analytical model for progressive mesh streaming  The main insight from the model  A sending strategy based on our model

24. 24 TdTd TdTd SiSi Assumptions  UDP + retransmission  Constant sending rate  We Retransmit lost packet as soon as packet loss is detected.  Packet loss is detected after time T d.

25. 25 i t =i t =0 0 Time  Receiver’s clock begins RTT/2 later (if packet is not lost, the sending time = the receiving time).  One unit time = time to send a packet.  If no retransmission, sending time = sequence number.

26. 26 Steps  Find the distribution of sending time receiving time decoding time

27. 27 T d : the time to detect packet loss p : the loss rate Sending Time  Sending Time S i is a random variable with Negative Binomial Distribution.

28. 28 TdTd TdTd SiSi S i +2T d Receiving Time: R i  R i = S i + nT d if it is retransmitted n times.  n is a random variable with geometric distribution.  We approximate S i using E[S i ].  R i = E[S i ] + nT d  The distribution of R i can be computed.  See the paper for detail.

29. 29 Packet iVertex v P (v): Decoding Time: D v  If an ancestor of vertex split v is inside a packet p, we say p is a parent packet of v.  Vertex split v can only be decoded when all packets in P(v) are received. P(v): the set of packet i and all parent packets of vertex v. Vertex v is in packet i

30. 30 In practice, we only consider j from S i to S i + 3T d. Packet j received at t Others received before t Decoding Time: D v

31. 31 After knowing D v  We can estimate the expected value of quality of a given 3D mesh as a function of time and packet loss probability.

32. 32 Verification of D v  We made two approximations: We use E[Si] to replace random variable Si in calculating Ri. We only add up to Si + 3Td instead of infinity in calculating E[Dv].  We use simulation to verify the accuracy after our approximations.  The difference between analytical result and simulation result is very small. 0.1223 in average 1.3083 in maximum (100000runs of simulation, loss rate: 10%)

33. 33 Outline  Background and motivation of our research  An analytical model for progressive mesh streaming  The main insight from the model  A sending strategy based on our model

34. 34 Sending Strategy and Quality Curve  Quality curve depends on D v.  D v depends on the sending order and dependency.  Sending strategy decides the sending order and hence the dependency among packets.  Different sending strategies generate different quality curves.

35. How much can we improve the quality if we choose a proper sending strategy?

36. 36  Worst Case vs. Ideal Case Consider Two Extreme Cases

37. 37 Worst Case vs. Ideal Case

38. 38 The Main Insight  The effect of dependency is only significant in the first few seconds.  Need to deal with dependencies only for interactive applications where this first few seconds matter: E.g., online games, building walkthrough

39. 39 What can we do?  Use a better sending strategy. Consider the effect of dependency Increase the initial sending rate Add FEC to initial data  Our model can be used to make the proper trade-off in all above cases.

40. 40 Outline  Background and motivation of our research  An analytical model for progressive mesh streaming  The main insight from the model  A sending strategy based on our model

41. 41 Greedy Strategy  We can calculate D v.  gain=w v (D v ’-D v )  Pack the vertex split with the maximum gain. DvDv Dv’Dv’ v Current Packet Next Packet ? ?

42. 42 gain=w v (D v ’-D v ) importance Effect of dependency Comparison of Greedy and FIFO  FIFO: Send the vertex splits in first-in-first out order (typically in the decreasing order of importance).  Greedy: Consider both importance and dependency.

43. 43 Average Quality (Td = 40, p = 0.1)

44. 44 In 90% Cases, the quality is better than (Td = 40, p = 0.1)

45. 45 Greedy FIFO Results

46. 46 Conclusion  Retransmission is important in Progressive mesh streaming.  The effect of packet loss exists even with retransmission and it depends on the dependency.  The effect of dependency is significant in first few seconds.  We can improve the initial quality with better strategy than FIFO.

47. Thank You! Q & A Time

48. 48 FIFO average Greedy 90% cases Greedy average FIFO 90% cases Results