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This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2011 proceedings

Layered Video Transmission using Wireless Path Diversity based on Grey Relational Analysis Abdul RAZZAQ

Ahmed MEHAOUA

LIPADE, Laboratoire d’informatique Paris Descartes 45 rue des saints pères 75006 Paris, France [email protected]

LIPADE, Laboratoire d’informatique Paris Descartes 45 rue des saints pères 75006 Paris, France [email protected]

Abstract — Scalable video applications have been gaining popularity during last years. Providing high quality video over wireless networks towards heterogeneous receivers is a challenging task due to erratic and time-varying nature of a wireless channel. Compressed video bit-streams, such as Scalable Video Coding (SVC), are very vulnerable to channel disturbances when transmitted over error-prone wireless channels. Transmission errors not only corrupt the current frame, but also propagate to next frames along motions prediction path. In this paper we propose a novel scheme for transmission of SVC-based streams over Wireless Mesh Network (WMN) with Unequal Error Protection (UEP) based on Appropriate Path Selection (APS) and Network Coding (NC). The scheme tries to select appropriate paths based on Grey Relational Analysis (GRA) for different SVC layers. It chooses the best path for most important layer, base layer, and less stable paths for enhancement layers. Some nearby nodes along the transmission path are selected for recoding their received packets and store them in buffers for some period of time. The nearby nodes coded packets can be transmit to receivers for recovering the lost packets. Keywords-Grey relational analysis; network coding; scalable video coding; unequal error protection; wireless mesh network.

I.

INTRODUCTION

Multimedia applications have received a lot of attention during last years. Wireless networks are becoming suitable platforms for exchanging real-time video streams. Fix bandwidth allocation, bit errors and burst-packet-loss in wireless networks are main focus of many researchers from past few years till present. Wireless transmission at high packet rates is often characterized by a burst-packet-loss behaviour, i.e., if one packet is lost there is more chance that consecutive packets will also be lost. Congested routers are one typical source for packet losses. In real time transmission packets arriving too late at the receiver are another source of packet loss. These losses severely influence the quality of multimedia applications. Any packet with errors can cause decoding failure and also propagate to next frames along motions prediction path [1]. Scalable Video Coding an extension of H.264/MPEG4-AVC [2] is a very good solution

to alleviate the effects of the error-prone and time varying channels in wireless networks. The aim of SVC is to extend the hybrid video coding approach to create a compressed bit stream decodable at different bit-rates with different computational power and display capabilities. The basic SVC design can be classified as layered video codec. Data of a compressed video bit stream is structured in different layers. As in wireless networks bit errors and packet losses are common, so therefore the base layer, which is most important layer, must be strongly protected as compare to enhancement layers. Different techniques such as error control and concealment unequal error protection [3] are available for transmitting the SVC. A novel adaptive unequal error protection for scalable video over wireless networks [4] was proposed by Naghdinezhad et al. The adaptive UEP and packet size assignment for scalable video transmission over burst-error channels [5] can be used to protect the different layers according to their importance. When the link between two nodes gets error, which will be usually burst rather than random, so there is high probability that bad channel conditions will persist for a period, which results to loss of multiple frames. So in such scenario the Forward Error Correction (FEC) techniques can not provide enough protection for base layer. Cooperation among nodes has been proposed to address these problems [6]. Dianati et al. proposed a cooperative ARQ scheme for ad-hoc networks [7]. Wu et al. proposed an adaptive scheme for transmission of SVC over wireless networks [8]. Research on network coding started recently as a promising framework to improve network throughput with a pioneering paper by Ahlswede et al. [9]. Normally, a router can merely route or forward messages. Each message on an output link is exactly the same copy of a message that arrived earlier on an input link. Network coding is based on a simple basic idea to allow each node in a network to perform some computation. Therefore, in network coding, each message sent on a node’s output link is a function or mixture of messages that arrived earlier on the node’s input link as shown in Fig. 1. After the pioneering paper [9] network coding's popularity is increasing and many research papers have appeared on the subject [10, 11, 12, 13]. Both UEP and NC are considered promising to improve the multimedia quality in error-prone wireless

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This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2011 proceedings

y1

f1(y1, y2, y3)

y2

f2(y1, y2, y3)

y3

be determined by calculating the Grey Relational Coefficient (GRC) according to the level of similarity and variability. The sequence with the largest GRC is the most desirable one. This technique is also effective for selection of appropriate paths for different layers of SVC according to their importance. GRA is usually implemented by following six steps: 1.

Classifying the networks parameters by two situations (smaller-the-best, larger-the-best) Defining the upper and lower bounds of the parameters Normalizing the parameters Defining the ideal situation Calculating the GRC Ranking the available paths according to the GRC values

2. Figure 1: Example showing network coding

networks. In this paper we propose a novel scheme, which is an extension of our previous work [14], for transmission of SVC-based streams over wireless mesh networks with unequal error protection, based on appropriate path selection, and network coding. The scheme tries to select appropriate paths based on grey relational analysis for different SVC layers. It chooses the best path for most important layer, base layer, and less stable paths for enhancement layers. Some nearby nodes along the transmission path are selected for recoding their received packets and store them in buffers for some period of time. The nearby nodes coded packets can be transmit to receivers for recovering the lost packets. Rest of the paper is organized as fellows: Section II presents our proposed scheme for transmission of SVC-based streams over wireless mesh networks based on UEP and NC. The simulation results are presented in Section III. Finally we conclude in Section IV.

3. 4. 5. 6.

For the purpose of selecting appropriate paths for different layers, we consider the network-layer metrics such as delay (ζ), jitter (θ), loss rate (σ) and throughput (α). Delay, jitter and lose rate belong to the smaller-the-best category while throughput belongs to larger-the-best. Before calculating the GRC, the data need to be normalized to eliminate the dimensional units. Assuming that n possible network paths (P1, P2, . . . ,Pn) are compared, and each network path has k parameters, the upper bound (uj) is defined as max{P1(j), P2(j), . . . , Pn(j)} and the lower bound (lj) as min{P1(j), P2(j), . . . ,Pn(j)}, where j = 1, 2, . . . , k. For smaller-the-best parameters the normalized value of Pi(j) parameter can be calculated as follows: ∗

II.

P i( j) =

PROPOSED SCHEME

In this work we consider a wireless mesh network in which our objective is to maximize the perceived video quality through appropriate path selection and network coding. The nodes are fixed relatively in WMN and can run a measurement module to measure the performance of the links from one WMN node to its neighbors. The WMN also runs a link-state protocol so that each node is aware of the latest state in all WMN links. The relationships between nearby nodes are relatively settled and topology is usually stable. First based on node state information of nearby nodes the scheme tries to select appropriate path based on grey relational analysis for different layers and secondly selection of nearby nodes along the transmission path to perform the network coding and retransmit the resultant network coded packet to user terminals. As the nodes are able to send their state information, so source node gets the state information and calculates the network topology for appropriate path based on grey relational analysis between source and destination. Grey System Theory was introduced in [15] to analyze the relationship grade from discrete sequences and select the best sequence. One of the sequences is defined as reference sequence presenting the ideal situation. The grey relationship between the reference sequence and the other sequences can

u

− pi( j)

j

u

j

− l

(1)

j

Similarly, for the larger-the-best parameters the normalized value Pi(j) can be calculated as follows: ∗

P i( j) =

pi( j) − l

j

(2)

u j − lj Network path attributes can be represented as a row matrix, where the elements of the matrix are the normalized values of k different network path attributes. ∗ ∗ ∗ ⎡∗ ⎤ P = ⎢ P (1 ), P ( 2 ), P ( 3 ), " , P ( k ) ⎥ ⎣ ⎦

∗

(3)

While P i ( j ) parameters are maximized in 1, the most preferable network path can be always described as P*1(j) = 1, where j = 1,2, . . . , k, and k is the number of network path parameters used for the decision making. Using the behavior of the normalizing algorithm, the ideal network path can be determined as S = [1, 1, 1, . . ., 1]. If there are N available network paths to choose from, the previous row matrix (3) can be extended to a N x k matrix, which contains all the

This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2011 proceedings

parameters that play role in the appropriate network path selection procedure. The matrix can be determined as follows:

PN

∗ ∗ ∗ ⎤ ⎡ ∗ ⎥ ⎢ P1 (1 ), P1 ( 2 ), P1 ( 3 ), " , P1 ( k ) ∗ ∗ ∗ ⎥ ⎢ ∗ = ⎢ P 2 (1 ), P 2 ( 2 ), P 2 ( 3 ), " , P 2 ( k ) ⎥ ⎥ ⎢" " ⎥ ⎢ ⎢ P∗ (1 ), P∗ ( 2 ), P∗ ( 3 ), " , P∗ ( k ) ⎥ N N N ⎦ ⎣ N

works let’s suppose the source S1 wants to send packets B1, B2, E1 and E2 (base and enhancement layers packets respectively) to receivers R1, R2 and R3. However, at time of severe congestions some packets may get lost in collisions or may arrive too late as shown in Fig. 2.

(4)

The final step is to calculate the GRC as follows: GRC

i

=

1 k

∑

w

∗

j

Pi ( j ) − 1 + 1

(5)

j=1

Where wj is the weight of each parameter and i (1 ≤ I ≤ N) is the network index. The path with the largest GRC is the most appropriate path. The source node calculates the GRC for all available paths. As in SVC bit-stream different layers have different priority, the base layer has highest priority and enhancement layer one has lower priority than base layer and enhancement layer two has lower priority than enhancement layer one and so on. So according to priority of layers appropriate paths are assigned to each layer in such a way that base layer stream has highest priority so therefore, it should be transmitted through the highest quality path (the path with highest GRC value) and while highest enhancement layer has lowest priority so it should be transmitted through lowest quality path (the path with lowest GRC value) means more important data are transmitting through more reliable path with less error rate. In this way we are providing an unequal error protection to different layers of SVC and the base layer can be received more completely than other enhancement layers. Furthermore, to improve the quality of video at receiving end we use another scheme which selects the nearby nodes along the transmission path for recoding packets for retransmission to receivers for recovering lost packets. This scheme will help to improve the video quality at receiving end as we are transmitting the enhancement layer with high error rate. In wireless mesh network all nodes are not always equally busy; there are some nodes which have relatively free resources than other nodes, so these nodes can be used as nearby nodes along with transmission path for network coding. To understand the method for node selection, consider Fig. 2, where n1, n2, n3, n4, and n5 are wireless mesh router nodes. S1 is source node and R1, R2 and R3 are destination nodes (receivers). The n1 and n3 are nodes in enhancement layer path while n2 and n5 are nodes in base layer path. We assume that n4 node is in communication range of n3, n5 and Receivers R1, R2, R3. In such scenario if n4 has relative free resources so we can select n4 as nearby node for network coding. It is used to XOR the adjacent sub-streaming packets and store resultant packets in buffer for unit period of time. To give the readers a feel how it

Figure 2: Appropriate path selection for different layers and nearby node selection for opportunistic network coding

As shown in Fig. 2, the receiver R1 received B1, B2, receiver R2 received B2, E2 and receiver R3 received B1, E1 and E2. Now the key question is what combination of packets should be code to maximize throughput. There could be multiple options with node n4, but the aim is to select the best one. The node n4 has 4 packets in its queue. It can send B1 which is decodable by only receiver R2. The option B1 B2 can be decode by R2 E2 is decodable by R1 and R2 while the and R3, while B1 B2 E2 can be decode at R1, R2 and R3. So the option B1 last option is the best one. Here we borrowed an algorithm Network Coding for Video (NCV) from [16] to make an opportunistic network coding decision at node n4. Then this nearby node sends the best network coded packets to the receivers. The error packets retrieve time is limited by its deadline for those packets which have deadline restriction during transmission. The nearby node will clean up the buffer according to the deadline of the SVC packets. The nearby node will reduce the number of retransmissions and as well as probability of error rate for SVC sub-streams. While selecting the nearby node for network coding, we look that the nearby node must have more free resources, it should be able to receive two adjacent sub-layers, packet error retrieve rate must be less than the packets deadline and nodes with shorter distance to the destination are chosen with higher priority to be the nearby nodes. Multiple nearby nodes selection is also allowed. The first batch nearby nodes is limited to the following equation:

t1l = t1

f

+ t1 s

t 1 l < t d − t ss − t c

(6)

Where t1l , t1f, t1s, td , tss and tc are restriction time of first batch cooperative nearby nodes, transmit time of request from user

This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2011 proceedings

terminal to first batch nearby nodes, packets transmit time from the first batch nearby nodes to user terminal, packets deadline time, streaming packets transmission time from the source node to receiver, and check error time in receiver respectively. III.

EXPERIMENTAL RESULTS

In this section, we present simulation results in order to evaluate the performance of proposed scheme in different scenarios. We used NS2 network simulator for SVC-based streaming over wireless mesh network. We used SVC bitstream with one base layer and two enhancement layers. We have all necessary information about all nodes in the network. Twelve nodes are used to construct a wireless mesh network with one source node and three receiver nodes in the range of 100 x 100 Sqm. The code rate of base layer, enhancement layer one and two are 200, 400 and 400 kbps respectively. We simulated our approach for three different scenarios in order to analyse the performance of proposed scheme. For the simplicity we do not consider any background traffic in our experiment. First SVC-based streams are transmitted without selecting appropriate paths and nearby nodes for opportunistic network coding. In second scenario streams are transmitted with selection of appropriate path for different layers while without selection of nearby node for network coding and finally with selection of both appropriate paths for different layers and nearby node. The parameters and their normalized values used for the appropriate path selection decision making are shown in Table 1. According to the normalization equations (1 and 2) of the GRA-based decision making, we can fine 0 and 1 normalized values for each path parameter. Normalized value 1 means that the network offers the best performance from the given parameter point of view, while 0 means the worst performance. By calculating the GRC by equation (5) the ranking order for all available paths can be determined. According to table 1, path number 2 is the best path and path 3 is the worst one. As in SVC bit-stream different layers have different priority, the base layer which carries most important part of video data has highest priority and enhancement layer one has lower priority than base layer and enhancement

layer two has lower priority than enhancement layer one and so on. So according to priority of layers appropriate paths are assigned to each layer in such a ways that base layer stream has highest priority so therefore, we transmitted it through the highest quality path 2 and while highest enhancement layer (enhancement layer 2 in this case) has lowest priority so we transmit it through second last lowest quality path 1. Means we assigned path two, four and one to base layer, enhancement layer one and enhancement layer two respectively. This means we are transmitting more important data through more reliable path with less error rate. The simulations results for all scenarios are shown in Fig. 3. As shown in Fig. 3(a) for without selection of nearby node for network coding and appropriate path for different layers, packet error probability for all three layers is almost same. Fig. 3(b) show the results for scenario two in which only appropriate path selection is allowed. As we can see that the most important layer, base layer, is received more correctly than other enhancement layers because it was transmitted by most stable path. But in this case the quality of video at receiving end cannot be improved enough because we are transmitting the enhancement layers on less stable paths, thus resulting in high packet error probability. To cope with this flaw, we used the approach of selecting nearby node to perform the network coding. And furthermore, we applied NCV for making intelligent decisions at nearby node. As shown in Fig. 3(c), selection of nearby node for performing opportunistic network coding can help to reduce error rate. If the permitted latency becomes longer, the reconstructed video quality becomes better at receiving end because longer deadline permits long time to retrieve error packets from more nearby nodes. Fig. 3 (d) shows the comparison of all three scenarios. As all results show that by using nearby node for opportunistic network coding, the received correct packets rate as in case three, is higher than other two scenarios. The received network coded packets at the receiver are decoded to be analysed. Fig. 4 shows the perceived video quality for crew sequence in case of third scenario while Fig. 5 shows PSNR graph for first 80 frames under all scenarios. As we can see from PSNR graph, there is a significant improvement in video quality under scenario 3 (with appropriate path selection and opportunistic network coding).

TABLE 1: PARAMETERS FOR DIFFERENT PATH SELECTION Delay (ζ) Available Paths

Loss Rate (σ)

Jitter (θ)

Throughput (α)

GRC ms

1

0.334

2

0.409

3

0.324

4

0.349

50 193 170 98

norm

ratio

norm

ms

norm

mbps

norm

1

0.0004

1

27

0

5

0.011

0

0.03

0.702

10

1

80

0.854

0.161

0.013

0.873

12

0.882

4

0

0.664

0.1

0

19

0.470

93

1

This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2011 proceedings

90

Received Rate

Received Rate

90

80

70

Base Layer

60

E1 Layer

80

70

Base Layer

60

E1 Layer

E2 Layer 50

20

40

60

80

100

120

140

E2 Layer 50

20

40

Packet Deadline (ms)

60

(a) Scenario One

100

120

90

Received Rate

80

70

80

70

Base Layer

60

E1 Layer

Scenario 1

60

Scenario 2

E2 Layer 50

140

(c) Scenario Three

90

Received Rate

80

Packet Deadline (ms)

20

40

60

80

100

Packet Deadline (ms)

120

140

Scenario 3 50

20

40

60

80

100

Packet Deadline (ms)

(b) Scenario Two

(d) Comparison Figure 3: Simulation results for three different scenarios

(a)

Original Sequence

(b) Reconstructed Sequence Figure 4: Perceived video quality for crew sequence

120

140

This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2011 proceedings

[5]

Scenario 3 (Path Selection + Network Coding) Scenario 2 (Only with Path Selection) Scenario 1 (Without Path Selection & Network Coding)

YUV PSNR [dB]

38

[6]

[7] 36

[8] 34

[9] [10]

32

0

10

20

30

40

50

60

70

[11]

Frames Figure 5: PSNR graph for crew sequence under all scenarios

IV.

CONCLUSION

In this paper we presented a novel scheme for SVC-based streaming over wireless mesh network with unequal error protection based on appropriate path selection and network coding. The proposed scheme tries to select appropriate path based on grey relational analysis for different SVC layers according to their importance and secondly selection of nearby node to perform the opportunistic network coding decision with help of NCV algorithm. The nearby nodes then store these resultant network coded packet in buffer for unit period of time and retransmit error packets to receiving end terminals. In our proposed scheme the base layer which carries most important part of video data is protected more effectively in such a way that it is transmitted over network through most stable path. The scheme provides robust video transmission over wireless mesh network, improved video quality at receiving end and also saves network resources. ACKNOWLEDGMENT This work is supported by the ANR French National Research Project TOSCANE n° 06-RIAM-019.

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[4]

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