Wei Cheng, Wei Tsang Ooi
School of Computing, National University of Singapore.
Sebastian Mondet, Romulus Grigoras,
Geraldine Morin,
IRIT/ENSEEIHT, France.

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
2

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

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

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

Progressive Streaming
6

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

Progressive Streaming
 Base mesh + a series of vertex splits
Base mesh vs1 vs2 vs3 vs4 vs5 vs6 vs7 vs8
8

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

Representation of Dependency
 Directed acyclic graph (DAG) directed acyclic graph
Vertex split dependency
10

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

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

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

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
15

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

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.
17

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

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

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

Decoded Mesh Quality quality
 Area under the curve
0
D v w v t time
21

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

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
23

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
.
S i
T d
T d
24

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.
t =0 t =i i
0 t =0 t =i
25

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

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

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

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
P
(v):
Packet i Vertex v
29

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

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.
31

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%)
32

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
33

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.
34

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

Worst Case vs. Ideal Case
37

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
38

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.
39

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
40

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

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.
gain=w v
(D v
’-D v
) importance Effect of dependency 42

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

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

Results
FIFO
Greedy
45

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.
46

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