and city-wide communication, enterprise networking, and ... shown along the edges of the graph. ... network architecture, e.g. wireless mesh networks, relay-.
Video Transmission over Wireless Multihop Networks Using Opportunistic Routing Mei-Hsuan Lu*, Peter Steenkiste*† and Tsuhan Chen* *Carnegie
Mellon University/Department of Electrical and Computer Engineering, Pittsburgh PA, USA † Carnegie Mellon University/School of Computer Science, Pittsburgh PA, USA
Abstract—Wireless multihop networks comprise of mobile or stationary stations interconnected via an ad hoc multihop path. The dynamically self-organized and selfconfigured nature of such network provides a flexible alternative in a variety of contexts where the deployment of a fixed infrastructure may be impractical. While this type of network provides new opportunities in multimedia streaming, the high rate requirement coupled with stringent time constraints poses a certain range of challenges in deploying high quality video services. This paper presents an analytical model to study the performance of multihop video streaming using opportunistic routing, an emerging technique that achieves high throughput in the face of volatile wireless links. Specifically, we use a discrete-time Markov chain to model the expected number of transmissions needed for a successful delivery in a multihop network. This model produces a closed-form expression that is later validated by the Monte-Carlo simulation. Using the proposed model, we compare opportunistic routing with the traditional routing method which considers the best predetermined multihop path. We demonstrate performance gains in throughput, latency, as well as decoded video quality from adopting opportunistic routing. The problem of out-of-sequence delivery inherently resulted from opportunistic communication is also discussed.
I. INTRODUCTION Wireless multihop networks [1-3] comprise of mobile or stationary stations interconnected via an ad hoc multihop path. Each node operates not only as a host but also as a router, forwarding packets on behave of other nodes that may not be within the direct radio range of their destinations. The dynamically self-organized and self-configured nature of such network provides a flexible alternative in a variety of applications where the deployment of a fixed infrastructure may be impractical, for example, broadband home networking, community and city-wide communication, enterprise networking, and sensor networks. Recently, video streaming over wireless multihop networks [4-5] has drawn significant attention due to the popularity of high-speed IEEE 802.11 wireless LANs (WLANs) [6-7]. While this type of network provides new opportunities in multimedia streaming services, the high rate requirement coupled with stringent time constraints poses a certain range of challenges in this context. Among recent advances, opportunistic routing has been appearing as an appealing multihop routing method which gives high throughput in dynamic wireless environments [8-12]. The idea is to exploit spatial diversity in combating scare spectrum resources and channel
variations. Traditional routing methods (with or without the use of path diversity) construct one or more static endto-end paths before transmitting data packets. This path is then repeatedly used for the corresponding sourcedestination pair as long as the route remains sustainable [15-16]. Adopting a different philosophy in route selection, opportunistic routing chooses the closest node1 toward the destination to forward the packet out of the set of nodes which actually received the packet. This results in high expected progress per transmission. The flexibility of opportunistic routing enables agile adaptation in fastchanging wireless environments, which is particularly suitable for serving high-rate and delay-sensitive video traffic.
0.5
S
R 0.3
0.8
D
Fig. 1. A toy multihop network with link delivery probabilities shown along the edges of the graph.
We use a toy example to illustrate the basic idea of opportunistic routing. Consider the network shown in Fig. 1 where S and D represent the source and the destination, respectively. Conventional multi-hop routing algorithms (e.g. [13-14]) select the direct path because it takes minimal hop count, a route metric that has been widely argued for its inefficiency. Recently-proposed routing metrics [16-17] end up with using the two-hop route (with R as the intermediate node) because it offers higher throughput and lower error rate than the direct path. In this example, the two-hop route statistically takes 3.25 transmissions to successfully deliver a packet while the direct path needs 3.33 transmissions. The static two-hop route is the best predetermined multihop path. Although a good route metric does increase performance, there is room for further improvement. Due to the broadcast nature of the wireless medium, D occasionally overhears transmissions from S to R (with a successful ratio of 30%). For those packets that luckily arrive at D through the direct path, there is no need to forward them from R to D again. Opportunistic routing takes advantage of such opportunities to increase network utilization, which results in 2.0 transmissions on average 1 To simply the explanation, we assume the best forwarder is the one that locates closest toward the destination, among those which have overheard the packet. However, the result presented in this paper can be applied to any channel propagation model.
for a successful delivery. Opportunistic routing can be considered as a special multipath routing scheme because each packet may traverse through different intermediate nodes. Differing from static multipath routing, it chooses the next-hop from the set of nodes which have already received the packet. Because the set of receiving nodes is not predictable, the actual path is not predetermined. Generally, as more nodes participate, better performance can be achieved due to a higher degree of spatial diversity. Opportunist routing is especially suitable for delivering video data for the following reasons: i) Because shorter paths are opportunistically exploited, average hop count is reduced. This allows higher rate and lower distortion. Also, reduced latency in transmitting through fewer nodes results in fewer late arrivals. These factors jointly contribute to superior user-perceived video quality at the destination. ii) Opportunistic routing allows successive packets being transmitted along different paths, which potentially generates out-of-sequence delivery at the destination. In TCP-based flows, such phenomenon may trigger retransmission or even worse congestion control; thus degrades the end-to-end throughput [18]. Fortunately, video applications generally employ UDP as the underlying transport protocol. The impact of out-of-sequence delivery can be compensated by introducing startup delay with sufficient buffering space at the destination. iii) Because ad-hoc network nodes are typically distinguished by their limited power, processing, and memory resources, transmitting large amount of video traffic over multiple hops implies a considerable expense of resources at the intermediate nodes. Opportunistic routing allows the existence of multiple next-hop candidates, which brings an opportunity for load sharing among resourceconstrained nodes. iv) Since the route is not fixed during the progress of a video session, opportunistic routing offers robustness in combating link breakage and node outage that typically calls for route reconstruction in the static multihop routing. Excessive latency induced during route reconstruction may cause buffer underflow at the video client and result in freezing of playback. In this paper, we present an analytical model to study the performance of multihop video streaming using opportunistic routing. Although many opportunistic routing protocols [8-12] have been proposed in the literature, to the best of our knowledge, this work is the first effort that analytically formulates the performance of opportunistic routing. Specifically, we use a discretetime Markov chain to model the expected number of transmissions needed for a successful delivery. A closedform expression is produced and later validated by the Monte-Carlo simulation. The resulting model enables us to abstractly quantify the performance of opportunistic routing such that it is mathematically tractable and can be easily extended to any application-specific performance analysis. It is worth mentioning that the proposed analysis can also be used to model opportunistic relaying [19-21], a MAC-layer scheme that exploits one or more intermediate nodes to relay a failed transmission. In fact, this model is applicable to opportunistic communications over any type of multihop
network architecture, e.g. wireless mesh networks, relayenabled WLANs, ad hoc mobile networks, or sensor networks. Using the proposed model, we compare opportunistic routing with the conventional static routing method which considers the best predetermined multihop path. Evaluation results demonstrate that opportunistic routing outperforms the best predetermined multihop routing in throughput, latency, as well as the decoded video quality. The inherit problem of out-of-sequence delivery induced by opportunistic routing is also discussed. The rest of this paper is organized as follows. Section II discusses related work in the context of multihop wireless streaming. Section III presents our proposed analytical model that compares opportunistic routing with the best predetermined multihop routing. Section IV demonstrates the performance evaluation results based on the proposed analytical model, in particular, from the perspective of video applications. Finally, Section V provides conclusion and outline of future work. II. RELATED WORK The daunting challenges in multihop wireless streaming have spurred a large body of research on design, analysis, and improvement in this area. In [15], Golf et al. investigated adding proactive route selection and maintenance to on-demand ad hoc routing algorithms. The early initiation of route discovery alleviates, if not eliminates, packet loss and late arrivals during route disruption experienced in static on-demand multihop routing protocols. This preventive behavior allows graceful route reconstruction and avoids sharp quality degradation. A similar technique is proposed in [16], in which a SNR-based “hand-off” concept is integrated into the Ad hoc On-demand Distance Vector (AODV) routing protocol to prevent disconnection during route maintenance. Recently, path diversity emerged as an efficient alternative for providing robustness in video communications over ad hoc networks [22-25]. This mechanism involves the adaptive selection of one or more network paths for transmissions of video packets using an overlay network architecture at the application layer. Path diversity has been combined with many error-resilience streaming techniques such as forward error control (FEC), selective ARQ, or/and adaptive reference picture selection [22] to advance improvement of video quality. Additionally, researchers have integrated path diversity with multiple description coding (MDC) and layered coding (LC) [22-25] as a form of cross-layer approach to support ad-hoc video streaming. The MDC/LC techniques exploit multiple link-disjoint paths to reduce the correlation of packet loss. This way, the impact of link breakage is alleviated by using other good routes to deliver independent descriptions of coded video. The tradeoff is that encoding a single video sequence into multiple descriptions or multiple layers must sacrifice some compression efficiency. Joint congestion-distortion optimized transmission [2627] aims to find the optimal rate for video streaming that achieves the optimal tradeoff between compression quality and self-inflicted congestion when video data is delivered through one or more paths. The best operating point is resolved to achieve the highest user-perceived
quality in accordance with a rate-distortion model. This mechanism is useful when there is a bottleneck link on the path from server to client where packet delays can be strongly affected by congestion resulting from previous transmissions. All the abovementioned works rely on using one or more predetermined paths to increase robustness in delivering delay-sensitive video data. In other words, they exploit spatial diversity of wireless networks which is made possible because different paths usually have different instantaneous channel conditions for the same shared medium. Opportunistic routing can be considered as an extreme multipath routing mechanism that completely exploits spatial diversity on a per-transmission basis. III. ANALYTICAL MODEL In this section, we present an analytical model to formulate the performance of the best predetermined multihop routing and opportunistic routing. Specifically, we study the expected number of transmissions needed for a successful delivery in an ad-hoc multihop network. The chosen metric can be considered as the reciprocal of the normalized effective throughput; hence it is a reasonable performance indicator. This metric is similar to the ETX (Expected Transmission Count) of a route defined in [17] for the unipath routing scenario, differing in that here only the forward delivery probability is considered because the reverse feedback channel is assumed to be error free. To simplify the explanation, in the rest of this paper, we use the term ETX to represent our metric of interest unless explicitly stated. To maximize the throughput, the best predetermined multihop routing simply uses ETX as the route metric. The optimal route is the one with minimal ETX. For opportunistic routing in which packets of a particular source-destination pair are not necessarily routed along a single path, the next-hop node is the one with minimal cost of moving a packet from itself to the destination. In other words, this is the node with the minimal route ETX toward the destination, chosen from the set of receiving nodes. We propose a discrete-time Markov model that produces a closed-form expression of the performance metric for opportunistic routing. Our analysis is based upon several assumptions. A perfect reverse channel is assumed since acknowledgement messages are small and generally coded with conservative modulation schemes. We further imagine that there are no collisions and packet loss is only due to the channel conditions, eliminating the role of scheduling of transmissions. In practice, communication systems with time-division multiple access (TDMA) and frequency-division multiple access (FDMA) fit into this model. Finally, we assume perfect node prioritization is achievable at each transmission, that is, we can always choose the best node out of all that have overheard the packet as the forwarder. Though all these assumptions narrow the focus of our study, we believe it still reveals valuable insights into the utility of opportunistic routing for wireless multihop networks. We use the four-node network given in Fig. 2 to illustrate the model variables in the following analysis.
P01
1 P12
P03
0 P02
P13
3 P02
2
Fig. 2. A four-node network with link delivery probabilities shown along the edges of the graph. In this graph, node 0 and node 3 are the source and the destination; node 1 and node 2 are candidate forwarding nodes.
A. Analysis for the Best Predetermined Multihop Routing For the best predetermined multihop routing, the optimal route is the one with minimal ETX. From [17], the ETX of a link is the predicted number of data transmissions required to deliver a packet over that link, including retransmissions. The ETX of a route is the sum of the ETX for all the links that constitute that route. Consider a multihop path l and Pl is the packet delivery probability of a composing link l in l . Given the assumption of a perfect reverse channel, the optimal path is obtained by solving the single-source shortest path problem. The network is modeled by a directed graph in which the edge weight is simply the EXT of the link, that is, ETXl =
1 Pl
(1)
where each attempt to transmit a packet is considered as a Bernoulli trial. Using Dijkstra’s shortest path search algorithm, we can obtain the ETX of the best predetermined multihop route as ETX MH = min( l
1
∑ P ). l∈l
(2)
l
Table I gives an example of link delivery probabilities for the network in Fig. 2. The link quality is assumed to be symmetric, i.e. Pij = Pji. The resulting ETX of all the possible routes are shown in Table II, which suggests the best predetermined multihop route that gives the minimal route ETX (4) is the two-hop path (0→2→3). TABLE I. EXAMPLE OF LINK DELIVERY PROBABILITIES (LDP) FOR THE NETWORK GIVEN IN FIG. 2 Link LDP
P01 0.25
P02 0.5
P03 0.1
P12 0.6
P13 0.8
P02 0.5
TABLE II. THE ETX OF ALL THE POSSIBLE ROUTES FOR THE NETWORK GIVEN IN FIG. 2 Path ETXMH
0,3 10
0,1,3 5.25
0,2,3 4
0,1,2,3 4.92
0,2,1,3 7.67
meaning of system states implies the following rules in state transition: i) A state transition is impossible if a bit is set in the outgoing state but the corresponding bit in the incoming state is not set (i.e. if a candidate forwarding node has already received the packet, it keeps the packet until the packet has been successfully delivered). This implies the transition probability matrix is a lower triangular matrix with several zero elements appearing below the main diagonal. ii) Once the sink state is reached, the state transition terminates (i.e. the destination has successfully received the packet). This implies the right most column vector in the transition probability matrix must have the form:
B. Analysis for Opportunistic Routing Deriving the expected number of transmissions for a successful delivery in opportunistic routing is not trivial. This is because, theoretically, a packet can travel through any sequence of nodes with any number of transmissions over a lossy link. This task becomes extremely difficult when a significant number of nodes participate in the routing process. We use a discrete-time Markov chain to model opportunistic routing, which produces a closed-form expression of the metric of interest. Consider a network composed of N nodes with source node labeled as 0 and destination node labeled as (N-1). The system state Xn is defined as the bit vector of packet reception status for all the possible receivers (candidate forwarding nodes and the destination node) after n transmissions with one (1) means a reception and zero (0) means a miss of the packet. The reception or loss of the packet at node k is corresponding to the k-th bit in the bit vector. States with the (N-1)-th bit set are sink states and they are compacted into one single state. The resulting model is a (2N-2+1)state Markov chain. Physically speaking, the initial state represents the case when the source is attempting to send a new packet; and the sink state corresponds to the case when the destination has successfully received the packet. Every state transition is a (re)transmission of the packet. Hence the goal of this analysis is to find the expected number of state transitions going from the initial state to the sink state. Table III gives the system states of the four-node network depicted in Fig. 2. Each column corresponds to a system state. Following the above elaboration, it should be straightforward to generate analytical results for any network topology.
[0
node 1 2 3
s0
s1
s2
s3
s4
0 0 0
1 0 0
0 1 0
1 1 0
0/1 0/1 1
T
Let A = {aij} be the transition probability matrix in which aij corresponds to the transition probability from state j to state i. To simply the explanation, here we assume nodes labeled with a smaller number have a larger route ETX toward the destination. Later we will describe how to generalize this model when this assumption is released. This allows us always choosing the largestnumbered node out of the set of receiving nodes as the next-hop, that is, the forwarding node corresponds to the most significant non-zero bit in the binary representation of the current state. The state transition probabilities are derived based on the reception status of the outgoing state and the link probabilities with respect to the forwarding node in the incoming state. We can write the transition probability matrix of the four-node network in Fig. 2 as ⎡ P01 P02 P03 ⎢ ⎢ P01 P02 P03 A = ⎢ P01 P02 P03 ⎢ ⎢ P01 P02 P03 ⎢ P 03 ⎣
TABLE III. SYSTEM STATES FOR A FOUR-NODE NETWORK WHERE S0 IS THE INITIAL STATE AND S4 IS THE SINK STATE state
0 " 1] .
0
0
0
P12 P13
0
0
0 P12 P13
P21 P23 P21 P23
0 P23
P13
P23
P23
0⎤ ⎥ 0⎥ 0⎥ ⎥ 0⎥ 1 ⎥⎦
(3)
where the notation x = 1 − x is used for compact representation. The corresponding state transition diagram is depicted in Fig 3.
Having defined the system state, we can write the state transition probabilities accordingly. The physical
P13
P01 P02 P03
P12 P13
s0
P01 P02 P03
1
P23
s2
s1 P01 P02 P03
P12 P03
P21 P23
s3
s4 P23
P21 P23
P23
P01 P02 P03
P03 Fig. 3. State transition diagram of the four-node network given in Fig.2, where s0 is the initial state and s4 is the sink state.
N = 12
N=7
5
10 9
4
7
Monte-Carlo Simulation
Monte-Carlo Simulation
8
6 5 4 3
3
2
1
2 1 0
0
1
2
3
4 5 6 Analytical Model
7
8
9
10
0
0
1
2 3 Analytical Model
4
5
Fig. 4. Comparison of the analytical model and the Monte-Carlo simulation on the expected number of transmissions for a successful delivery.
With the initial state probability
P (0)
⎡ Pr( X 0 = s0 ) ⎤ ⎡1 ⎤ ⎢ Pr( X = s ) ⎥ ⎢ ⎥ 0 1 ⎥ ⎢ ⎢0 ⎥ = ⎢ Pr( X 0 = s2 ) ⎥ = ⎢0 ⎥ , ⎢ ⎥ ⎢ ⎥ ⎢ Pr( X 0 = s3 ) ⎥ ⎢0 ⎥ ⎢ Pr( X = s ) ⎥ ⎢0 ⎥ 0 4 ⎦ ⎣ ⎦ ⎣
we can iteratively obtain the n-th step state probability as P ( n ) = A n P (0) . Also, it can be observed that the system will eventually stay at
P(∞)
⎡ Pr( X ∞ ⎢ Pr( X ∞ ⎢ = ⎢ Pr( X ∞ ⎢ ⎢ Pr( X ∞ ⎢ Pr( X ∞ ⎣
= s0 ) ⎤ ⎡ 0 ⎤ = s1 ) ⎥⎥ ⎢⎢ 0 ⎥⎥ = s2 ) ⎥ = ⎢ 0 ⎥ . ⎥ ⎢ ⎥ = s3 ) ⎥ ⎢ 0 ⎥ = s4 ) ⎥⎦ ⎢⎣1 ⎥⎦
The n-th step state probability reveals the likelihood of all the possible reception status for a particular packet after n transmissions. We are interested in the probability of the sink state, i.e. s4, in the n-th step, which corresponds to the probability of successfully delivering a packet with n or fewer transmissions Pr( X n = ss N −2 ) = Pr(ETX OR ≤ n) .
(4)
The right-hand side is essentially the CDF of ETX. From Eq. (4), we can obtain the probability function needed for successfully delivering a packet with exact n transmissions, that is Pr(ETX OR = n) = Pr( X n = ss N −2 ) − Pr( X n −1 = ss N −2 ) .(5)
Finally, the expected number of transmissions needed for a successful delivery is given by ∞
ETX OR =
∑ n ⋅ Pr(ETX n =1
OR
= n) .
(6)
Eq. (2) and Eq. (6) give the mathematical form of the performance metric for the best predetermined multihop routing and opportunistic routing, respectively. As mentioned earlier, the transition probability matrix (3) is generated based on the assumption that nodes labeled with a smaller number have a larger route ETX toward the destination. This may not hold true in a complex network scenario where multiple nodes can have equal route ETX toward the destination. When this assumption is released, Eq. (3) still holds if the tie is broken in such a way that the largest numbered node among all that have received the packet transmits first. The physical meaning of tie breaking is scenario dependent. For example, one may prefer nodes locating geographically closer to the destination as the forwarder in a tie. Before we present the performance evaluation results, we use the Monte Carlo simulation to validate the analytical model derived in this section. Note that the Monte Carlo simulation is independent from the analytical model; hence it gives objective results. In the simulation, hundred topologies are generated randomly for a particular size of network. Link delivery probabilities are approximated assuming 16-QAM is used in the free space path loss model [28]. In each topology, the number of transmissions needed for a successful delivery is averaged over 5,000 iterations (packets). For each transmission, a Bernoulli trial is performed to determine whether an eligible forwarding node overhears the packet, i.e. the random number is smaller than or equal to the link probability. This process is repeated until the destination node receives the packet. Fig. 4 compares the analytical model and the Monte Carlo simulation result on the expected number of transmissions needed for a successful delivery. Results of networks with 5 and 10 randomly positioned forwarding nodes are given here. Each point on the graph corresponds to a randomly generated topology. We can see that the proposed analytical model fits well with the Monte Carlo simulation results. Simulations results of scenarios with different numbers of candidate forwarders also suggest similar observations.
TABLE IV. NODE-TO-NODE LINK DELIVERY RATIOS USED IN THE PERFORMANCE EVALUATION WHERE ELEMENT (I, J) REPRESENTS THE LINK DELIVERY PROBABILITY FROM NODE I TO NODE J.
1 1 0.3 0.3 0.2 0.7 0.7 0.2 0.65 0.15 0.8 0.4 0.01
1 2 3 4 5 6 7 8 9 10 11 12
2 0.3 1 0.4 0.5 0.85 0.5 0.85 0.6 0.55 0.55 0.6 0.3
3 0.3 0.4 1 0 0.7 0.6 0.5 0.6 0.55 0.55 0.6 0.3
4 0.2 0.5 0 1 0.2 0.5 0.45 0.45 0.95 0.35 0.95 0.7
5 0.7 0.85 0.7 0.2 1 0.75 0.5 0.95 0.35 0.95 0.75 0.2
6 0.7 0.5 0.6 0.5 0.75 1 0.45 0.95 0.35 0.95 0.75 0.2
0.45
80
0.4
70
0.35
60
0.3
50
0.25 40 0.2 30
0.15
20
MH
0.1
OR
0.05
Gain from OR (%)
Normalized Effective Throughput
IV. PERFORMANCE EVALUATION In this section, we present the performance evaluation results for wireless video streaming in a multihop network. Opportunistic routing is compared with the best predetermined multihop routing. The results given in this section are drawn from the analytical mode elaborated in Section III. We use a mesh topology with node-to-node link delivery ratios shown in Table IV, which are real scenarios extracted from the Roofnet trace [30]. All the participating nodes are considered to be within the radio range of each other, i.e. any node pair can communicate directly with a non-zero probability. Symmetric channel conditions, though not necessarily required, are adopted. We choose node 1 and node 12 as the source and the destination and others as forwarding candidates. Candidate forwarding nodes are introduced into the network one by one from the lowest-labeled node (node 2) to the highest-labeled node (node 11) to create ten scenarios with varied degree of spatial diversity.
10
Gain from OR
0
0 1
2
3
4
5
6
7
8
9
10
Num of forwarding nodes
Fig. 5. Normalized effective throughput for the best predetermined multihop routing (MH) and opportunistic routing (OR). The latter outperforms the former by up to 71 %.
7 0.2 0.85 0.5 0.45 0.5 0.45 1 0.45 0.95 0.35 0.95 0.7
8 0.65 0.6 0.6 0.45 0.95 0.95 0.45 1 0.65 0.9 0.9 0.4
9 0.15 0.55 0.55 0.95 0.35 0.35 0.95 0.65 1 0.4 0.9 0.9
10 0.8 0.55 0.55 0.35 0.95 0.95 0.35 0.9 0.4 1 0.8 0.15
11 0.4 0.6 0.6 0.95 0.75 0.75 0.95 0.9 0.9 0.8 1 0.65
12 0.01 0.3 0.3 0.7 0.2 0.2 0.7 0.4 0.9 0.15 0.65 1
Although only a medium-sized network is used here, theoretically, our model can be applied to any multihop network topology. However, because the dimension of the transition probability matrix is exponentially proportional to the number of candidate forwarders, i.e. (2N-2+1)×(2N-2+1), the computational complexity can be too high to analyze a large-scaled network. To deal with such case, one can exploit certain properties of the transition probability matrix (e.g. the elements in the upper triangle are all zeros) to reduce the computational efforts. One can also employ the methodology of divideand-conquer to generalize the results presented in this section. A. Normalized Effective Throughput Fig. 5 shows the evaluation result of the normalized effective throughput for the best predetermined multihop routing (MH) and opportunistic routing (OR). The normalized effective throughput is defined as the reciprocal of the expected number of transmissions needed for a successful delivery. The result indicates that opportunistic routing generally performs better than the best predetermined routing, with gains up to 71%, which agrees with our intuition that opportunistic routing takes advantage of remote overhearing nodes that leads to more effective use of each transmission, especially when multiple paths with similar ETX exist. Opportunistic routing indicates a smooth improvement while the best predetermined multihop routing shows a stair-like jump as more candidate forwarding nodes are available for routing. This result essentially conforms to the simulation result proposed in [8]. The best predetermined multihop route for the network in Table IV is the twohop path (0→7→12) which corresponds to an ETX of 4.04. After node 7 is added to the network, the normalized effective throughput remains unchanged in the best predetermined routing since later introduced nodes do not constitute a better route. However, these nodes (i.e. node 8, 9, and 10) do contribute to the throughput improvement in opportunistic routing. On average, it takes 2.45 transmissions to deliver a packet in the case of opportunistic routing when all the candidate forwarding nodes are introduced.
Num of forwarding nodes: 5 1600
1600
1400
1400
1200
1200
1000
1000
800
800
600
600
400
400
200
200
0
0
10
20 30 Num of TXs (OR)
40
50
0
0
10
20 30 Num of TXs (MH)
40
50
Num of forwarding nodes: 10 2000
2000
1500
1500
1000
1000
500
500
0
0
10
20 30 Num of TXs (OR)
40
50
0
0
10
20 30 Num of TXs (MH)
40
50
Fig. 6. Histograms of the number of transmissions needed for a successful delivery over 5000 packets
Foreman CIF 30Hz
Stefan CIF 30Hz
50
50 MH (10Mbps) OR (10Mbps)
45
45
MH (5Mbps)
40 MH (10Mbps) OR (10Mbps)
35
PSNR (dB)
PSNR (dB)
OR (5Mbps)
40
35
MH (5Mbps) OR (5Mbps)
30
30
1
2
3
4 5 6 7 Num of relay nodes
8
9
10
1
2
3
4 5 6 7 Num of relay nodes
8
9
10
Fig. 7. Average PSNR value of the luminance (Y) component for two CIF format test sequences, Foreman and Stefan encoded with 30 fps. The GOP length is 15, composed of a leading I frame followed by 14 P frames. Results from two scenarios with nominal network bandwidth of 10 Mbps and 5 Mbps are given here.
B. Video Quality To evaluate the user-perceived video quality, we use two CIF format test sequences, Foreman and Stefan encoded with the publicly-available H.264/AVC reference codec [29] at 30 fps. The GOP length is 15, composed of a leading I frame followed by 14 P frames. The resulting video quality is obtained by adjusting the encoding bit rates in accordance with the available throughput. This is done in the CBR rate control module that conforms to the Hypothetical Reference Decoder (HRD) in the H.264 specification. Average PSNR values of the luminance (Y) component are shown in Fig. 7. Two scenarios with a nominal bandwidth of 5 Mbps and 10 Mbps are studied here. In general, the decoded video quality for both sequences coheres with the trend of the effective throughout presented in the previous subsection. The figure shows maximal marginal improvement of 2.8 dB and 3.4 dB for Foreman and Stefan, respectively from adopting opportunistic routing, when all the ten candidate forwarding nodes are available for the source-destination pair. C. Out-of-Sequence Delivery By far we have demonstrated the benefits of adopting opportunistic routing. This technique, however, inherently generates out-of-sequence delivery because successive packets belong to the same session can potentially traverse through different sequences of forwarding nodes. For unipath routing methods, we always have in-sequence delivery since all the packets of a single source-destination pair are routed through the same path2. To deal with out-of-ordered arrivals, we need to introduce startup delay with sufficient buffering space at the destination. Thus, it is important to understand the out-of-sequence degree incurred by opportunistic routing in a wireless multihop network. Fig. 8 illustrates the percentage of out-of-sequence degree. Differing with previous studies, this is a simulation result conducted with procedures similar to the Monte-Carol simulation in Section III. The out-ofsequence degree is defined as the difference of sequence 2
In the unipath routing, out-of-sequence delivery may still happen during route change. However, the out-of-sequence degree should be relatively small.
number in successive packets which arrive out of order. From the figure, the most serious out-of-sequence degree occurs in the case of two candidate forwarding nodes; and it gradually alleviates as more forwarding nodes participate. This is because packet arrival time is approximated using the number of transmissions to deliver a packet from source to destination, i.e. we omit the queuing delay and processing time incurred at each forwarding node. As more forwarding nodes participate, fewer transmissions are needed, thus restricting the outof-sequence degree to a narrower range. 60 2 fwd nodes 4 fwd nodes 6 fwd nodes 8 fwd nodes 10 fwd nodes
50
40 percentage (%)
Fig. 5 also suggests the use of a larger set of candidate forwarding nodes in ad hoc mulithop routing. As more nodes join the network, a new route with a smaller ETX may appear available for the best predetermined multihop routing. In the case of opportunistic routing, more candidate forwarders increase the chance of getting a closer overhearing node toward the destination. In either case, we can achieve superior performance when a higher degree of spatial diversity is available. Fig. 6 gives the histograms of the number of transmissions needed for a successful delivery over 5000 packets. This figure indicates the number of packets suffering from excessive retransmissions is reduced by using opportunistic routing, especially with a larger set of candidate forwarding nodes. Since fewer transmissions lead to shorter end-to-end latency as well as less congestion, opportunity routing suffers less packet loss and fewer late arrivals at the video client.
30
20
10
0
0
5
10
15
20
25
out-of-seq degree
Fig. 8 Percentage of out-of-sequence degree with different numbers of candidate forwarding nodes introduced into the network
D. Protocol Overheads The evaluation results presented in this section demonstrate gains resulting from opportunistic behavior. To put opportunistic routing in practice, one should also consider protocol overheads in performing distributed computing across multiple forwarding nodes in order to achieve an agreement of the next-hop forwarder. Effective protocol design should assure routing overheads do not overcome the benefit gained from opportunistic behavior. Some of these overheads are: 1) Estimating link quality: Each node needs to measure the link quality between itself and its neighboring nodes and use this information to determine the best forwarder for each source-destination pair. A common way of link quality measurement is through periodical broadcast probing. Each node monitors the reception/loss of probing packets and estimates the link delivery ratio accordingly. The amount of control traffic is dependent on the number of nodes and the frequency of node probing. 2) Scheduling forwarding nodes: With locally collected knowledge of link quality, a distributed algorithm is needed to schedule candidate forwarders such that the best overhearing node transmits first. The protocol must be robust enough so that disagreement due to packet loss generates rare duplicate forwarding. The overhead incurred from distributed scheduling is typically proportional to the number of participating nodes.
3) Dealing with collision avoidance and fairness: The protocol has to deal with short-term collision avoidance and long-term fairness across several concurrent sourcedestination pairs which may be in support of different numbers of forwarding nodes. Fulfilling this requirement is usually coupled with some performance loss. Although not reflected in the analysis, these overheads are critical in performance evaluation when real-world deployment is under consideration. Good protocol design should minimize the routing overheads such that gains from opportunistic operations are not overly compromised. E. Oppourtunistic Routing in Highly-Dynamic Environments Performance evaluation presented in this section are conducted in a static network topology in which link delivery probabilities (shown in Table IV) do not fluctuate over time. The evaluation results do not demonstrate benefits from using opportunistic routing in a highly-dynamic environment. In that case, opportunistic routing avoids latency induced from route reconstruction in the unipath routing [13-14] or static multipath routing [26-28] upon link breakage or node outage. This property is particularly important for video streaming applications because excessive delay may cause buffer underflow at the video client. This results in freezing of playback, an annoying effect perceived by end users. V. CONCLUSION AND FUTURE WORK Opportunistic routing has appeared as an appealing alternative for wireless multihop networks. This technique is especially attractive for wireless video streaming because it offers higher rates and lower latency, resulting in less distortion at the video client. In this paper, we investigated the performance of this promising routing scheme by comparing it with the best predetermined multihop routing. Specifically, a discretetime Markov chain is derived to model the expected number of transmissions needed for a successful delivery in an ad-hoc multihop network. Using the Markov model, a closed-form expression is produced and validated by the Monte-Carlo simulation. Although several opportunistic routing protocols have been proposed in the literature, to the best of our knowledge, this work presents the first analytical model that formulates the performance of opportunistic routing. The resulting model enables us to abstractly quantify the performance of opportunistic routing such that it is mathematically tractable and can be easily extended to any application-specific performance analysis. Based on the proposed model, we have demonstrated performance gains of opportunistic routing in achieving high effective throughput, reduced end-to-end latency, and improved decoded video quality. While the evaluation results are drawn without concerning implementation details, key protocol overheads are outlined for the consideration of effective protocol design. In addition, the inherit problem of out-of-sequence delivery is studied. When acceptable startup delay with sufficient buffer space is supported at the video client, opportunistic routing is a useful approach for video streaming over wireless multihop networks.
As part of future work, we intend to work on the design, implementation, and deployment of a practical opportunistic routing protocol, with the focus of improving user-perceived quality of video streaming applications. It is important to assure the complexity and overheads of the protocol do not compromise the gain from behaving opportunistically. We will study the effectiveness of this technique by performing various experiments in the real-world environment. ACKNOWLEDGMENT This work is supported in part by HP Labs, Palo Alto, CA and by iCAST (The International Collaboration for Advancing Security Technology). REFERENCES [1]
[2]
[3]
[4]
[5]
[6]
[7]
[8] [9] [10]
[11]
[12] [13]
[14]
[15]
[16]
I. F. Akyildiz, X. Wang, "A survey on wireless mesh networks," IEEE Communications Magazine, Volume 43, Issue 9, Sept. 2005 Page(s): S23 - S30. S. Lee, S. Banerjee, and B. Bhattacharjee, "The case for a multihop wireless local area network, " IEEE INFOCOM, Hong Kong, March 2004. D. Aguayo, J. Bicket, S. Biswas, G. Judd, and R. Morris, "Linklevel measurements from an 802.11b msh network," ACM SIGCOMM, Aug 2004. E. Setton, T. Yoo, X. Zhu, A. Goldsmith and B. Girod, "Crosslayer design of ad hoc networks for real-time video streaming," IEEE Wireless Communications Magazine, Aug. 2005, vol. 12, no. 4, pp. 59-65, Invited paper. N. Gogate, D.-M. Chung, S. S. Panwar, and Y. Wang, "Supporting image and video applications in a multihop radio environment using path diversity and multiple description coding," IEEE Trans. on Circuit and System for Video Technology, vol. 12, no. 9, pp. 777-793, Sept. 2002. IEEE Std. 802.11a. Supplement to Part11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications: High-speed Physical Layer in the 5 GHz Band, IEEE Std. 802.11a-1999. IEEE Std. 802.11g. Supplement to Part11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications: Higher-speed Physical Layer Extensions in the 2.4 GHz Band, IEEE Std. 802.11g-2003. S. Biswas and R. Morris, “Opportunistic routing in multi-hop wireless networks,” in HotNets-II, Nov. 2003. S. Biswas & R. Moriris, "ExOR: opportunistic multi-hop routing for wireless networks," ACM SIGCOMM 2005. P. Larsson, "Selection diversity forwarding in a multihop packet radio network with fading channel and capture," ACM SIGMOBILE 2001. M. Zorzi and R. R. Rao, "Geographic Random Forwarding (GeRaF) for ad hoc and sensor networks: multihop performance," IEEE Trans. on Mobile Computing, Volume 2, Issue 4, Oct.-Dec. 2003 Page(s): 349 - 365. R. R. Choudhury and N. H. Vaidya, "Mac-layer anycasting in ad hoc networks," ACM SIGCOMM 2004. J. Broch, D. Johnson, and D. Maltz, "The dynamic source routing protocol for mobile ad hoc networks", http://www.ietf.org/internet-drafts/draft-ietf-manet-dsr-03.txt, 1999 C. Perkins, E. Royer, and S. Das, "Ad hoc on demand distance vector (AODV) routing", http://www.ietf.org/internetdrafts/draftieft-manet-aodv-03.txt, 1999. T. Goff et al., “Preemptive routing in ad hoc networks,” in Proceeding of ACM/IEEE Mobile Computing and Networking (MOBICOM), July 2001. H. M. Tsai, N. Wisitpongphan, and O.K. Tonguz, "Link-quality aware AODV protocol," in Proc. IEEE International Symposium on Wireless Pervasive Computing (ISWPC), Jan. 2006.
[17] D. De Couto, D. Aguayo, J. Bicket, and R. Morris, "A highthroughput path metric for multi-hop wireless routing,” in Proc. ACM/IEEE MobiCom, Sept. 2003. [18] H. Lim, K. Xu, and M. Gerla, "TCP performance over multipath routing in mobile ad hoc networks," in Proceedings of IEEE ICC, May 2003. [19] M. Lu, P. Steenkiste, and T. Chen, "Time-aware opportunistic relay for video streaming over WLANs," to appear in Proc. IEEE International Conference on Multimedia and Expo (ICME), July 2007. [20] S Jain and S. R. Das, "Exploiting path diversity in the link layer in wireless ad hoc networks," IEEE International Symposium on World of Wireless Mobile and Multimedia Networks (WoWMoM), June 2005. [21] D. Chen, J. Deng, and P. K. Varshney, "A state-free data delivery protocol for multihop wireless sensor networks," IEEE Wireless Communications and Networking Conference, March 2005. [22] S. Mao, S. Lin, S. S. Panwar, Y.o Wang, and E. Celebi, "Video transport over ad hoc networks: multistream coding with multipath transport," IEEE Journal on Selected Areas in Communications, Special Issue on Recent Advance in Wireless Multimedia, vol. 21, no.10, pp.1721-1737, Dec. 2003. [23] J. G. Apostolopoulos, "Reliable video communication over lossy packet networks using multiple state encoding and path diversity", Proc. Visual Communication and Image Processing (VCIP), pp. 392-409, Jan. 2001. [24] M. van der Schaar, Y. Andreopoulos and Z. Hu, "Optimized scalable video streaming over IEEE802.11a/e HCCA wireless networks under delay constraints," IEEE Trans. on Mobile Computing, vol. 5, no. 6, pp. 755-768, June 2006. [25] D. Jurca and P. Frossard, "Packet selection and scheduling for multipath video streaming," to appear in IEEE Transactions on Multimedia, Volume. 9, NO. 2, April 2007. [26] X. Zhu, E. Setton and B. Girod, "Congestion-distortion optimized video transmission over ad hoc networks", Journal of Signal Processing: Image Communications, no. 20, pp. 773-783, Sept. 2005, Invited Paper. [27] P. A. Chou and Z. Miao, "Rate-distortion optimized streaming of packetized media," IEEE Transactions on Multimedia, Vol. 8, No. 2, April 2006. [28] Simon Haykin, "Digital Communication Systems", 4th Edition, John Wiley & Sons, 2004. [29] H.264/AVC reference software JM12.2, http://iphome.hhi.de/suehring/tml/, April 1, 2007. [30] MIT Roofnet, http://pdos.csail.mit.edu/roofnet
