Universal Network Coding-Based Opportunistic Routing for Unicast

Universal Network Coding-Based
Opportunistic Routing for Unicast
Abdallah Khreishah, Member, IEEE, Issa Khalil, Member, IEEE and Jie Wu, Fellow, IEEE
Abstract—Network coding-based opportunistic routing has emerged as an elegant way to optimize the capacity of lossy wireless
multihop networks by reducing the amount of required feedback messages. Most of the works on network coding-based opportunistic
routing in the literature assume that the links are independent. This assumption has been invalidated by the recent empirical studies
that showed that the correlation among the links can be arbitrary. In this work, we show that the performance of network coding-based
opportunistic routing is greatly impacted by the correlation among the links. We formulate the problem of maximizing the throughput while
achieving fairness under arbitrary channel conditions, and we identify the structure of its optimal solution. As is typical in the literature, the
optimal solution requires a large amount of immediate feedback messages, which is unrealistic. We propose the idea of performing
network coding on the feedback messages and show that if the intermediate node waits until receiving only one feedback message from
each next-hop node, the optimal level of network coding redundancy can be computed in a distributed manner. The coded feedback
messages require a small amount of overhead, as they can be integrated with the packets. Our approach is also oblivious to losses and
correlations among the links, as it optimizes the performance without the explicit knowledge of these two factors.
Index Terms—Network coding, wireless networks, cross-layer design, coded feedback, feedback, link correlation
Ç
1 INTRODUCTION
DESIGNING efficient protocols for wireless multihop
networks is not a straightforward extension from the
protocols designed for their wireline counterparts, due to
the unique properties of wireless links. One of the major
properties of wireless links is the poor link quality. For
example, recent studies [2] have shown that 50 percent of
the operational links in Roofnet [3] have loss rates higher
than 30 percent. Therefore, a major challenge for deploy-
ing wireless multihop networks is to design a transmis-
sion protocol that handles the lossy behavior of the
wireless links efficiently.
An efficient way of handling losses in wireless multihop
networks is to exploit the diversity among the links. Oppor-
tunistic routing [4] is the first trial to perform this exploita-
tion. In opportunistic routing, there is no specific path from
the source to the destination. Any node that overhears the
packet can relay it. Take Fig. 1 as an example in which
source node s wants to send packets to the destination d.
The labels on the links represent their delivery rates. If we
use traditional shortest path routing, the link between s
and the chosen relay node will be the bottleneck, and the
achievable rate will be upper bounded by 0.1. On the other
hand, if we allow the node that receives the packet to for-
ward it, the achievable rate will be 1 À ð1 À 0:1Þf
, which is
a huge improvement over shortest path routing. The main
challenge that faces the deployment of opportunistic rout-
ing is dealing with the case of when two relay nodes over-
hear the same packet. The work in [4] resolves this
problem by assigning priorities to the next-hop forwarders,
such that the node with higher priority will transmit first.
All of the other next-hop forwarders have to listen to the
transmission to decide whether one of the packets, over-
heard by a lower priority node, has been overheard by a
higher priority node. If so, the lower priority node will not
be responsible for forwarding the packet.
Performing opportunistic routing requires coordination
among the links and the design of a specialized MAC proto-
col. It also requires all of the next-hop nodes to be able to
overhear each other, which might not be available. In
Fig. 2a source node s1 wants to send packets to the destina-
tion node d1 through the relay nodes v1 and v2. Assume that
after the transmission of the a and b packets, node v1 has a,
and node v2 has both a and b. Node v2 has to know which
packets to send, and how many of each type, based on over-
hearing the transmission of node v1.
Intrasession network coding [5] can be used to resolve
the shortcomings of opportunistic routing. In intrasession
network coding, the source node divides the message it
wants to send into batches, each having K packets of the
form P1; . . . ; PK. The source node keeps sending coded
packets of the form
PK
i¼1 giPi, where gi; 8i is a random
coefficient chosen over a finite field of a large enough size,
typically 28
–216
. Upon receiving a coded packet, the inter-
mediate relay node checks to see if the coded packet is line-
arly independent to what it has received before. If so, the
node keeps the coded packet, otherwise it drops the packet.
Each intermediate node generates linear combinations of
the packets it has, and sends the resulting coded packets.
When the destination receives K linearly independent pack-
ets, it can decode all of the packets of the batch. Therefore, it
A. Khreishah is with the Department of Electrical and Computer Engineer-
ing, New Jersey Institute of Technology, Newark, NJ07102.
E-mail: abdallah@njit.edu.
I. Khalil is with the Qatar Computer Research Institute, Doha, Qatar.
E-mail: ikhalil@qf.org.qa.
J. Wu is with the Department of Computer Information Sciences, Tem-
ple University, Philadelphia, PA. E-mail: jiewu@temple.edu.
Manuscript received 11 Mar. 2013; revised 2 May 2014; accepted 5 May 2014.
Date of publication 7 May 2014; date of current version 8 May 2015.
Recommended for acceptance by L. Xiao.
For information on obtaining reprints of this article, please send e-mail to:
reprints@ieee.org, and reference the Digital Object Identifier below.
Digital Object Identifier no. 10.1109/TPDS.2014.2322617
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 26, NO. 6, JUNE 2015 1765
1045-9219 ß 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

sends feedback to the source that requests it to stop sending
from this batch and moves to the next batch. Intrasession
network coding resolves the opportunistic routing problem
due to the results in [6], which show that if the coding coeffi-
cients are chosen randomly over a large enough finite field,
any two packets will be linearly independent with a very
high probability. This property of random network coding
eliminates the unnecessary feedback and overhearing
requirements in opportunistic routing, and makes the
design of the MAC layer independent of the other layers. In
Fig. 2b the source node performs intrasession network cod-
ing. Instead of sending a it sends a þ b, and instead of send-
ing b it sends a þ 2b. If the same reception scenario happens,
node v2 does not need to overhear node v1 to make its own
decision. It can generate a linear combination of the packets
it has, say 2a þ 3b, which is linearly independent to the
packet that node v1 has. Therefore, if every relay node in
this example delivers only one packet succesfully to the des-
tination node, the whole batch (of size 2) can be recovered.
This simple example shows that intrasession network cod-
ing transfers the problem from ‘which and how many pack-
ets to send’ to only ‘how many packets to send’, which is
much easier to answer. The rest of the paper will be dedi-
cated to answering this question optimally. Interested read-
ers are encouraged to read the following references for
more information about the use of network coding in wire-
less networks [7], [8], [9], [10].
Despite the simplicity that intrasession network coding
creates for opportunistic routing, deciding the number of
coded packets that each node has to send is a big challenge.
The number of packets to be sent not only depends on the
loss rates of the links, but also on the correlation among the
links defined in [11]. To understand the challenge in choos-
ing the number of transmitted packets, we provide an
example that uses Fig. 3.
In this example, node s is the source node and node d is the
destination node. There are two paths that the packets can
follow from the source to the destination, and these paths
are separated by a lake. Therefore, nodes on one side of the
lake cannot overhear nodes on the other side. Each of the
two links, ðs; v1Þ and ðs; v2Þ, has a delivery rate of 0.5, and
we study three different cases as in [11]. Case 1: The two
links are independent. This means that the reception pro-
cess is independent among the links. Case 2: The two links
are positively correlated. This means that if one link is inac-
tive, the other one will be the same. Case 3: The two links are
negatively correlated. This means that if one of the links is
active, the other one will be inactive.
We use the following simple strategy. Each node stops
the transmission of packets when it is sure that its next-
hop nodes have collectively received the same number of
linearly independent packets that it has received. For sim-
plicity, we assume that the batch size is 6. For case 1, the
source node needs to try for eight transmissions in order
for both of the next-hop nodes to collectively receive the
full rank, as illustrated in Fig. 4a. For case 2, the source
node needs to try for 12 transmissions in order for both of
the next-hop nodes to receive full rank. This is because
both links will be active for the same six time slots and
inactive in another six time slots, as illustrated in Fig. 4b.
For case 3, the source node needs to try for six transmis-
sions in order for both of the next-hop nodes to receive full
rank. This is because the first link will be exclusively active
for three time slots, and the second one will be exclusively
active in the remaining three time slots, as illustrated in
Fig. 4c. Therefore, if we follow the previously mentioned
policy, the achievable throughput for case 3 will be
Fig. 2. An example representing the effectiveness of network coding for
opportunistic routing.
Fig. 3. A network with a lake in the middle, as used in the example.
Fig. 4. Illustration of the channel activation scenarios that insure that v1
and v2 collectively achieve full rank under different correlation conditions
between the channels.
Fig. 1. An example of a network to illustrate opportunistic routing.
1766 IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 26, NO. 6, JUNE 2015

approximately 1.33 times that of case 1, and twice that of
case 2. Note that the three rates can be made closer to
each other if v1 and v2 stop sending packets when their
next-hop nodes each receive three linearly independent
packets. This means that they should be given a credit of
only sending three linearly independent packets, regard-
less of the rank of the matrix that they have received.
Most of the previous works on opportunistic routing
with intrasession network coding either assume that the
links are independent and design the protocol based on that
[5], [12], [13], [14], or use the forwarding rule that says the
total number of received linearly independent packets
should equal the number of linearly independent packets
received by next-hop nodes [15]. In the above example, if
we assume the first assumption, then, in case 2, there will
not be enough linearly independent packets for the destina-
tion to decode because the source node did not send enough
packets. Also, if we assume independent links in case 3, the
throughput will be reduced by 1.33, due to the unnecessary
transmissions. On the other hand, the works that design the
rate control according to the rule that says the total number
of received linearly independent packets should be the
same as the ones received by next-hop nodes, result in
throughput reduction in both cases 1 and 2.
In a general network, the links will have different correla-
tions, and these correlations change over time, as is noted in
[11]. This makes it difficult to perform measurements about
the correlation, as to decide whether to use network coding
or not. Therefore, it is crucial to design a strategy that guar-
antees a good performance in all cases and can adapt to the
changes in the link qualities and the correlation among the
links. To that end, we tackle the above problem in this paper
and provide the following contributions:
We formulate the problem of utility maximization for
multiple unicast sessions that use network coding-
based opportunistic routing on an arbitrary wireless
multihop network, and use the duality approach to
come up with the optimal distributed solution.
We identify the challenges of implementing the
optimal distributed algorithm to come up with a
more practical algorithm. The practical algorithm
works in a batch-by-batch manner and performs
network coding on the feedback messages to exploit
the broadcast nature of wireless links in the reverse
direction. This reduces the number of feedback mes-
sages and eliminates the need for immediate feed-
back information. The algorithm is universal, as it
takes into account the loss rates and the correlations
among the links without the need to explicitly mea-
sure them.
We prove that the batch-by-batch algorithm con-
verges to the optimal solution.
We present simulation results for our algorithm
under different wireless settings, and show its supe-
riority regardless of the channel’s characteristics.
It should be noticed that there are other works that have
considered the correlation among the links. The works in
[16] and [17] solve a similar problem with a similar formula-
tion. However, they assume a complete, immediate, and
non-lossy feedback. Our paper resolves this very important
practical problem by applying network coding to the feed-
back messages. We prove that by using this feedback mech-
anism, our solution converges to the optimal solution.
In the report [18], MORE can be extended to include
the correlation among the links. However as shown in
[16] Section 3.5, the objective of MORE is to minimize the
number of transmissions not maximizing the throughput
or achieving fairness. Also, in MORE, if the links are not
independent, we need to measure the correlation among
the links. This is different from our approach that con-
verges to the optimal solution, without the need to explic-
itly know or measure the correlation among the links,
which is not practical.
The work in [15] uses the coded feedback approach.
However, the back-pressure algorithm they used is heuris-
tic without any theoretical guarantee and does not take
into account the correlations among the links. This differs
from our approach that is proven to converge to the
optimal solution.
2 SETTINGS
In this paper, we consider a network represented by a set
of nodes V . The links between the nodes are lossy and
time-varying. A transmission by a node can be received by
any subset of next-hop nodes. We represent this by a
hyperedge ðu; JÞ, where u is the node that performs trans-
mission, and J is a subset of the set of next-hop nodes.
There are N unicast sessions in the network, each with a
source si, a destination di, a rate Ri, and a utility function
UiðRiÞ, 8i 2 f1; . . . ; Ng. Similar to most of the opportunistic
routing protocols [5], [12], [14], [15], we are interested in
the transmission of large files. Therefore, the throughput is
the most important factor, and the individual packet delays
are of no importance.
Since we are using intrasession network coding, one
important factor to decide is the rate of linearly indepen-
dent packets that a node has to successfully deliver to
next-hop nodes. To model this factor, we use the concept
of credits,1
much like in [5], [16]. The symbol Xi
uv is used
to represent the rate of credits transferred from node u to
v for session i, which is the rate of linearly independent
packets that node v has to deliver to next-hop nodes out
of the linearly independent packets it has received from
node u. Therefore, the total rate of credits for session i at
node v would be
P
u2V Xi
uv, and these credits will be dis-
tributed to the next-hop nodes of v. We also use ai
u to rep-
resent the fraction of time in which node u is scheduled to
send the packets of session i. Symbol RuJ represents the
rate of packets that are sent by node u and are received
by any of the nodes in the set J. Assume that there are K
next-hop nodes for node u, then there are 2K
À 1 combi-
nations of next-hop nodes. Any hyperedge J here belongs
to one of the 2K
À 1 combinations. Symbol RuJ here repre-
sents the rate of distinct packets sent by node u and
received by the next-hop nodes in J. This depends on the
1. There is a slight difference between the meaning of credit here
and its meaning in [5]. In [5], it represents the number of linearly inde-
pendent packets that have to be sent by a node. Here, it represents the
number of linearly independent packets that have to be delivered suc-
cessfully to next-hop nodes.
KHREISHAH ET AL.: UNIVERSAL NETWORK CODING-BASED OPPORTUNISTIC ROUTING FOR UNICAST 1767

bandwidth of node u, the delivery rates of the links
between u and each next-hop node in J and the correla-
tion among these links.
3 PROBLEM FORMULATION
Our problem can be formulated as follows. We want to
maximize:
XN
i¼1
UiðRiÞ;
Subject to:
X
v2V
Xi
vu À
X
v2V
Xi
uv
ÀRi; u ¼ si;
0; else;

8i; 8u 2 V ndi (1)
X
v:v2J
Xi
uv ai
uRuJ 8i; 8u 2 V ndi; 8ðu; JÞ: (2)
We assume that the utility function UiðRiÞ is non-
decreasing and strictly concave. If the utility function is cho-
sen properly, maximizing the objective function will achieve
different kinds of fairness among the sessions [19]. Exam-
ples of UiðRiÞ would be: wilogð1 þ RiÞ and wi
R
1Àg
i
1Àg , where
0 g 1, and wi is the weight assigned for session i.
Here, ai
u depends on the underlying interference model.
Typically, it corresponds to the convex hull of all of the
achievable rates at all links [20]. Generally, the correspond-
ing optimal scheduling policy is NP-hard [21] and approxi-
mation algorithms are used. In this paper, scheduling is of
secondary importance, and we use the simple IEEE 802.11
protocol in the simulations. We use this so that we can
focus on the network coding part, and so we can have a
fair comparison with the other approaches that use the
IEEE 802.11 protocol [5], [15].
Algorithm 1: Selecting the Packet to Send
1: Zi
v 0; 8i; 8v
2: for i 1 to N do
3: Sort next-hop nodes according to (qi
u À qi
v).
4: Remove the nodes with negative backlog
5: for Each sent packet P do
6: Choose node v with the highest positive backlog
difference such that yiT
v Ã P is zero.
7: set Zi
v Zi
v þ 1
8: end for
9: set wi
P
v Zi
vðqi
u À qi
vÞþ
10: end for
11: select iÃ
arg maxiwi.
12: send a packet from session iÃ
ai
u is a variable that depends on the MAC as well as
the network layer’s decisions. This is implied from the
way the protocol works. Note that, working in a slot-by-
slot manner, the value of aðtÞ will be either 0 or 1. If
node u is scheduled and decides to send from session i
using Algorithm 1, ai
u at that time slot will be 1. Other-
wise, it will be 0. If, we average ai
uðtÞ over time, the
result will be the optimal ai
u.
The ﬁrst set of constraints represents balance equations
for the credits, so that the total received credits at a node
should be equal to the total amount of sent credits. This guar-
antees that node di will receive linearly independent packets
at a rate of no less than Ri. The second set of constraints rep-
resents the fact that if a packet is received by many nodes,
only one of them can use this packet to increase its credits,
which is a unique property of the wireless links.
Note that the constrains do not mean that the total num-
ber of sent linearly independent packets should be equal to
the total received ones, due to the constraints set (2). For
example, in the three cases in Fig. 3, if more than one node
receives the packet, only one of them gets credit for that
packet. Therefore, for each batch, the source node has six
credits, and it distributes them evenly among its next-hop
nodes. Even though both v1 and v2 receive linearly indepen-
dent packets of rank 4, 6, 3, for cases 1, 2, 3, respectively,
this also does not mean that the number of transmissions
the source node has to make in the three cases should be the
same. Therefore, the protocols that assume independent or
negatively-correlated links perform poorly under other
conditions.
According to the previous discussion, if there is only one
session in the network, the achievable rate will be the max-
ﬂow between the source and the destination, regardless of
the channel conditions. When more than one session exists
in the network, then by the time-sharing variables ai
u, the
rate region, represented by the formulation, will be the max-
imum rate region that intrasession network coding can
achieve. This is because intrasession network coding does
not allow coding between different sessions; hence, the best
thing to do is to allow time sharing among the sessions.
Since the constraints are linear, we have a convex optimi-
zation problem. The following proposition allows us to use
the duality approach to solve the problem [22], [23].
Proposition 1. Formulations (1)-(2) represent a convex optimi-
zation problem. Also, there is no duality gap between the pri-
mal and dual problems.
Proof. The constraints represent an intersection of halfspa-
ces, which represent a convex set. Also, we are maximiz-
ing a concave objective function. Therefore, the problem
is a convex optimization problem. Due to the fact that the
Slater conditions hold, there is no duality gap, i.e., the
optimal solution to the dual problem is the same as the
optimal solution to the primal problem. tu
In the supplementary document, which can be found
on the Computer Society Digital Library at http://doi.
ieeecomputersociety.org/10.1109/TPDS.2014.2322617, we
present a mapping from any wireless networks to an
equivalent wireline network, such that the the capacity
properties are preserved. With this conversion, we show
that our formulation, achieves the maximum possible rate
region with intrasession coding.
4 THE BASIC ALGORITHM
4.1 Structure of the Optimal Solution
Ignoring the scheduling constraints, we associate a
Lagrange multiplier qi
u with each constraint in (1). This

results in the following Lagrange function:
Lð~R; ~X;~qÞ ¼
XN
i¼1
UiðRiÞ À qi
si
Ri
À
X
i;u
qi
u
X
v:v2V
Xi
vu À
X
v:v2V
Xi
uv

subject to (2).
After applying simple changes of variables, the Lagrange
function becomes
Lð~R; ~X;~qÞ ¼
XN
i¼1
½UiðRiÞ À qi
si
RiŠ þ
X
u;i
X
v
À
qi
u À qi
v
Á
Xi
uv
subject to (2).
The Lagrange function is separable [23], which means
that the problem can be solved in a distributed way by
using the gradient method, as follows.
Source algorithm. Each source si selects its rate at each
time slot as follows:
RiðtÞ ¼ arg max
Ri
½UiðRiÞ À qi
si
ðtÞRiŠ: (3)
Intermediate node algorithm. Each intermediate node u selects
the number of credits for session i to transfer to all of its
next-hop nodes, at each time slot, as follows:
fXi
uvðtÞgv2V ¼ arg max
~X
X
v2V

qi
uðtÞ À qi
vðtÞXi
uv

(4)
subject to:
X
v2J
Xi
uv ai
uRuJ 8i; 8J: (5)
Dual variables updates. The dual variables can be updated in
a distributed way, as follows:
qi
uðt þ 1Þ ¼ qi
uðtÞ þ bi
u
X
v2V
Xi
vuðtÞ À
X
v2V
Xi
uvðtÞ

þ Ri Â 1u¼si
!þ
(6)
Here, ½:Šþ
is a projection on the positive real numbers,
and b is the step size.
5 OUR PRACTICAL SOLUTION
In this section, we develop a practical protocol based on the
structure of the optimal solution that we introduced in the
previous section. To do so, we first specify the challenges of
directly implementing the basic algorithm that was
described in the previous section, and then we provide our
practical solution.
5.1 Challenges
The Algorithm, represented by the operations in ((3)-(6)),
converges to the optimal solution; however, it has the fol-
lowing shortcomings.
The first challenge is that the algorithm requires a large
amount of feedback messages. For example, if a node that
has l next-hop neighbors sends k packets from the batch, we
need ðk Â lÞ feedback messages per batch. Also, the node
that has l previous-hop neighbors needs to send different
feedback messages to each one of these neighbors. Given
that the wireless links are lossy further increases the chal-
lenges of the problem.
The second challenge is that the algorithm is based on
slot-by-slot updates, which means that after sending a
packet, a node has to get immediate and accurate feedback
from all of the next-hop nodes, which is also impractical.
We resolve the first challenge by noting that the transmit-
ted packets are coded packets. Therefore, we can compress
the feedback into one coded packet that represents all of the
received packets, which we will describe next. Therefore,
we exploit the broadcast nature of wireless links in the
reverse direction of transmission. We resolve the second
challenge by performing the updates in a batch-by-batch
manner instead of performing the updates on each time
slot, as we will describe next.
5.2 The Coded Feedback Approach
The coded feedback approach has been used previously
by many works [15], [24], [25]. The work in [25] per-
formed the coded feedback approach over multihop in
wireline networks, i.e., network coding is performed on
the feedback message, and these feedback messages are
allowed to travel over more than one hop in the reverse
paths between the source and the destination. The objec-
tive in [25] was to find the min-cut max-flow in wireline
networks. For wireless networks, the works in [15], [24]
use the coded feedback message at the immediate previ-
ous-hop node to perform rate control. In [24], the coded
feedback approach is used for multicast, while in [15] it is
used for the unicast case. In both cases, rate control was
based on a heuristic, and no proof of the the optimality
was provided. In this paper, we limit the coded feedback
messages to be used by the previous one-hop-away
nodes, much like [15], [24], but we show that our method
achieves the optimal solution.
The common way of using the coded feedback is
through the null space. The null space of the matrix A is
the linear space of vectors such that the result of multi-
plying any one of them by A equals zero. For example,
if y belongs to the null space of A, then yT
A ¼ 0, where
yT
is the transpose of y.
Take Fig. 5a as an example, in which node s sends four
coded packets. Node v receives two of them. Node v can
compute the null space of the space of the packets it
receives, choose a vector from this space, and send it back
to node s. As is illustrated in Fig. 5b: node s can now multi-
ply this vector with each of the packets it has sent. If the
result is zero, node s can infer that the packet has been
received by node v with high probability. Otherwise, node s
knows that the packet has not been received by node v.
Using a hash table, the work in [15] makes the false-positive
probability very low, about 10À10
.
Note that in the coded feedback approach, only one feed-
back message from the node can acknowledge all of its

previous one-hop away nodes; therefore, it allows for the
exploitation of the broadcast nature of wireless networks in
the reverse direction of transmission. Also, as we will
explain, these coded feedback messages can be integrated
with the original packets with very low overhead.
5.3 Integrating the Coded Feedback Approach with
the Algorithm
In this section, we move to apply the gradient algorithm
that we adopted in the previous section in a batch-by-
batch fashion. Therefore, the index t will refer to the
batch number.
For the rate update (3) to be implemented in a batch-by-
batch manner, the source will inject all of the packets of a
given batch with the same rate specified by (3). Also, (4) and
(6) can be done in a batch-by-batch manner by performing
the transfer of the credits for a given batch, and the queue
length updates at a node after making sure that the next-hop
nodes have collectively received linearly independent pack-
ets equal to the number of credits for that batch at that node.
After that, the node will move to the next batch. Therefore,
what needs to be specified is how to use the coded feedback
approach at the relay node u to perform the following two
decisions that lead to maximizing (4), subject to (5).
Node u has to decide the session that the current
packet should be sent from.
Node u has to also decide the number of credits to be
assigned to each next-hop node.
To perform the first decision optimally, the relay node
should choose session iÃ
that achieves the maximum value
for the following among all of the sessions.
È
Xi
uvðtÞ
É
v2V
¼ arg max
~X
À
qi
uðtÞ À qi
vðtÞ
Á
Xi
uv (7)
Subject to:
X
v2J
Xi
uv ai
uRuJ 8ðu; JÞ: (8)
Note that in (7), the objective function is a linear function of
Xi
uv. In order to perform maximization while satisfying (8),
for every session i, node u ranks the next-hop nodes v
according to the backlog difference ðqi
u À qi
vÞ. Then it gives
as many virtual credits2
to this next-hop node with the high-
est backlog difference, subject to (8). Then it continues to do
the same thing for the remaining nodes according to their
backlog difference. For every sent packet, the next-hop
node v gets a virtual credit if node v has received the packet,
and no other node with a higher backlog difference has
received the packet. This is because (8) means that, if more
than one node receives a packet, only one of them can use
that packet to increase its number of credits. Therefore, we
give the credit to the node with the largest backlog. This
process can be checked by using the coded feedback
approach. Let us denote the number of virtual credits for
session i and node v by Zi
v; then node u calculates
wi ¼
P
vððqi
u À qi
vÞZi
vÞ, such that all vs have positive backlog
differences. Then node u selects the session that achieves
the maximum wi. Algorithm 1 describes the above strategy.
In the algorithm, yi
v represents a randomly selected vector
from the null space of the ith session packet at node v. To a
large extent, Algorithm 1 determines ai
uðtÞ.
Every time a node receives a vector in the null space from
the next-hop node, it multiplies that vector with all of the
packets it has sent. This is done so that it can figure out the
number of linearly independent packets that has been
received by next-hop nodes. Once that rank becomes equal
to the number of credits assigned for that batch at that
node, the node distributes its credits to next-hop nodes in a
fashion similar to that in Algorithm 1. However, this time
the node only focuses on one session, and the credits that
are assigned are real, not virtual credits. Algorithm 2 repre-
sents the credit assignment algorithm. In the algorithm,
TAKENðPÞ ¼ 1 means that a credit for packet P has been
assigned for one of the next-hop nodes that received packet
P. Therefore, in line number 7 of the algorithm, no other
next-hop node can be assigned a credit for this packet.
Algorithm 2: Credits Assignment
1: Sort the next-hop nodes according to the backlog
difference
2: Discard the nodes with negative backlog difference.
3: for Each sent packet P do
4: TAKENðPÞ 0
5: end for
6: for Each next-hop node v with positive backlog
difference in descending order do
7: for Each packet P do
8: if TAKENðPÞ ¼ 0 AND yiT
v Ã P ¼ 0 then
9: Ci
v Ci
v þ 1
10: TAKENðPÞ 1
11: end if
12: end for
13: end for
The example in Fig. 5a shows a broadcast link with one
source node s and three receiving nodes, u, v, and w. The
Fig. 5. An example representing our coded feedback approach.
2. Note that these are different from the actual credits that will be
distributed as a strategy for the second decision the node has to per-
form. These credits are just for knowing the packet of which session
should be sent. Also, these credits are not transmitted to next-hop
nodes. They are just computed locally.

queue length of every node is represented in the figure.
The figure also shows the four coded packets sent by s,
and the coded packets received by the next-hop nodes. It
also shows one vector in the null space3
of the space of the
received vectors by each node. Fig. 5b shows the result of
multiplying the y vectors—which represent the null space
of the received packets—with the packets at node s. If a
cell in the table in Fig. 5b is filled, this means that the mul-
tiplication result of the packet in that row with the y vector
of the next-hop node in that column is zero. This also
means that the next-hop node in that column has over-
heard the packet in that row.
In Fig. 5a, the coded packets at the sender represent the
sent packet from a specific batch and session, and the coded
packets at the receivers are for the same session and batch.
In Algorithm 1, node s first sorts the next-hop nodes accord-
ing to the backlog difference in descending order which
results in the following order, v; u, then w. Node s then mul-
tiplies the vectors in the null spaces of each one of these
nodes, with each one of the sent packets. The result of the
multiplication is illustrated in Fig. 5b. Then, node s assigns
Zi
v ¼ 2 because node v has the highest backlog and has over-
heard two packets. Then node s assigns Zi
u ¼ 0, because all
of the packets that are received by node u have been
received by nodes with higher backlog differences. Using a
similar approach, node w gets Zi
w ¼ 1. Therefore, the total
weight obtained by Algorithm 1 for this session is wi ¼ 38.
On the other hand, for the scenario in Fig. 6a, node s assigns:
Zi
v ¼ 2, Zi
u ¼ 1, Zi
w ¼ 0, and wi ¼ 42.
In Fig. 5a, assume that node s has three credits to be dis-
tributed to next-hop nodes. Using the results on the table,
the next-hop nodes of node s have collectively accumulated
packets with ranks equal to the credits at the node. This can
be verified from the table in Fig. 5b. Therefore, node s will
assign the next-hop nodes the credits for this batch, and
move on to the next one. Node v gets two credits, as it is the
node with the highest backlog difference, and it has over-
heard two packets. Node u cannot be assigned a credit
because the packet it has overheard has also been overheard
by a node with a greater backlog difference. Therefore, the
algorithm gives one credit to node w, due to the overhearing
of the fourth packet, “4X1 þ X2 þ 2X3.” Fig. 6a represents
the same example as Fig. 5a, but instead of making the links
between s and u; v, respectively, positively correlated, we
make them negatively correlated. Therefore, node u in this
example receives “4X1 þ X2 þ 2X3” instead of
“4X1 þ 5X2 þ X3.” Since no credit has been assigned to
node v for the packet “4X1 þ X2 þ 2X3,” node u can be
assigned one credit. The examples represented by Figs. 5
and 6 show the adaptability of our algorithm to different
channel conditions and correlations among links.
6 EVALUATIONS
6.1 Simulation Settings
In this section, we evaluate the performance of our protocol
in wireless networks with different network characteristics.
We study the effect of varying different parameters on the
performance of the network. These parameters are the loss
rates of the links, the correlations among the links, and the
number of sessions in the network. We are interested in the
following two performance metrics: the total throughput
observed by all of the sessions, and the fairness in allocating
rates among the flows. We compare our protocol to two
other protocols in the literature that represent the state-of-
the-art opportunistic routing scheme with network coding.
These are MORE [5] and CCACK [15]. In MORE, the
authors assume that the links are independent. Based on
that, the nodes in MORE periodically estimate the channels’
loss rates, which allows the nodes to compute an estimation
of the number of transmissions that each node has to per-
form. CCACK, on the other hand, uses the coded feedback
approach. However, the rate control mechanism in CCACK
is heuristic, and does not take into account the correlation
among the links. We used MATLAB to perform the simula-
tions in this section.
6.2 Results on an Illustrative Topology
We simulate one session using the topology in Fig. 3 with
sixteen nodes. We vary two parameters: the delivery rates
of all of the links, and the correlation between the links of
the source node. The delivery rate values change from 0.3 to
0.8. For each one of these delivery rate values, we make the
correlations between the links independent with k ¼ 0, as
defined in [11], positively correlated with k ¼ 1, or nega-
tively correlated with k ¼ À1. We assume very large files.
Therefore, we run the simulations until the steady state
throughput is reached, and then we record that value.
Fig. 7 illustrates our results. Our universal approach
(UNIV) improves the throughput from 40 to 300 percent
over both MORE and CCACK, depending on the scenario.
The biggest improvement is noticed when the loss rates of
the links are very high. This is due to the use of coded
feedback in an optimal manner, which does not require
too many feedback messages. The highest throughput is
achieved by our protocol when the links are negatively cor-
related, whereas the lowest throughput is achieved when
the links are positively correlated. This is due to the fact
that the source node has to send more packets when the
links are positively correlated, as explained in Section 1.
Note that the difference between the positively correlated
Fig. 6. An example representing our coded feedback approach.
3. The coefficients for the y vectors here are over the real numbers
just for illustration. When the algorithms are implemented, finite fields
are used, and the negative value is changed depending on the size of
the finite field.

and the negatively correlated cases is not large, because all
of the nodes, except the source, are performing the same
operations in both cases, as also explained in Section 1.
The gain of our protocol is not only due to the use of the
coded feedback approach, because CCACK, which uses
this approach, performs similarly to MORE under these
settings. In conclusion, the strength of our protocol lies
behind integrating the coded feedback approach with
cross-layer optimization.
Typically, the gain from network coding is higher
when the links are negatively correlated. However,
MORE performs slightly better when the links are inde-
pendent. This is because MORE computes the number of
packet transmissions that each node has to perform based
on the assumption that they are independent. CCACK, on
the other hand, achieves its highest performance when
the links are negatively correlated, due to the use of the
coded feedback approach.
We performed another set of simulations on the same
topology in Fig. 3 with two opposite sessions, such that
the source of one of them is the destination of the other one.
The throughput results are similar to those with one session,
as the two sessions share the network, and fair end-to-end
rates were achieved by all of the schemes.
This motivates us to study bigger network topologies.
6.3 Results on Grid Topologies
We perform simulations on grid topologies of sizes 3 Â 3
and 4 Â 4. We change the delivery rates of the links in the
range of 0:4 to 0:8, and the correlation coefficient among the
links as defined in [11] in the range À1 to 1. We also change
the number of unicast sessions from one to four sessions in
the 3 Â 3 topology, and from one to six sessions in the 4 Â 4
topology. The results for the 3 Â 3 topology are in Fig. 8,
and the results for the 4 Â 4 topology are in Fig. 9.
In the plots we measure either the average throughput or
the average utility function value on different topologies
that have the same parameter (the x-axis of the figure). Our
utility function is ui ¼ logð1 þ RiÞ, which is used to repre-
sent a combined factor of the total throughput and the util-
ity as in [19], [26], [27].
As can be seen from both of the figures, the performance
of our scheme increases with the number of sessions, while
in the other two schemes, there is no significant perfor-
mance increase when we increase the number of sessions.
This is due to the session scheduling policy that we use.
The utility or fairness index follows the same pattern, due
to the same reason. The gain of our scheme is very low
when we have one session in the network, and it becomes
almost 100 percent when we increase the number of ses-
sions. Another observation from the simulations is that our
scheme takes advantage of the correlation among the links,
as there is a difference between the case when g ¼ À1 and
when g ¼ À0:5, while this is not noticeable for the other
schemes. This is due to the credit assignment algorithm
used. In the supplementary document, available online, we
added more simulation results.
7 CONCLUSIONS AND FUTURE WORK
In this paper, we have developed a distributed opportunistic
routing algorithm that uses network coding. The design of
our algorithm is inspired by recent results in the literature
Fig. 8. Simulation results on a 3 Â 3 grid.
Fig. 7. Simulation results for the topology in Fig. 3 with one session.

that showed the sensitivity of the opportunistic routing and
network coding protocols to the correlations among the wire-
less links. Our algorithm adapts to changes in the channel
loss rates and the correlations among the links.
ACKNOWLEDGMENTS
This research was supported in part by NSF grants ECCS
1331018, CNS 1065444, CCF 1028167, CNS 0948184, CCF
0830289, and Emirates Foundation grant EF 2009/075. Part
of this work was presented in SECON 2012 [1].
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Fig. 9. Simulation results on a 4 Â 4 grid.

Abdallah Khreishah received the BS degree with
honors from Jordan University of Science Tech-
nology in 2004. He received the PhD and MS
degrees in electrical and computer engineering
from Purdue University in 2010 and 2006, respec-
tively. In Fall 2012, he joined the ECE department
of New Jersey Institute of Technology as an assis-
tant professor. His research spans the areas of
network coding, wireless networks, congestion
control, cloud computing, and network security.
He is a member of the IEEE.
Issa Khalil received the BSc and MS degrees
from Jordan University of Science and Technology
in 1994 and 1996, and the PhD degree from Pur-
due University in 2006, all in computer engineer-
ing. He joined the Faculty of Information
Technology (FIT) of the United Arab Emirates Uni-
versity (UAEU) in August 2007, where he was pro-
moted to an associate professor in September
2011. In August 2012, he was appointed as the
chair of the Information Security department at
UAEU. In June 2013, he joined the Qatar Comput-
ing Research Institute (QCRI) as a senior scientist in the cyber security
group. His research interests span the areas of wireless and wireline
communication networks. He is especially interested in security, routing,
and performance of wireless Sensor, Ad Hoc, and Mesh networks. He is
a member of the IEEE.
Jie Wu is the chair and Laura H. Carnell professor
in the Department of Computer and Information
Sciences, Temple University. Prior to joining Tem-
ple University, he was a program director at the
National Science Foundation and was a distin-
guished professor at Florida Atlantic University.
His research interests include wireless networks
and mobile computing, routing protocols, fault-tol-
erant computing, and interconnection networks.
He has published more than 600 papers in various
journals, conference proceedings, and books. He
serves on the editorial board of the IEEE Transactions on Computers
and the Journal of Parallel and Distributed Computing. He has served as
an IEEE computer society distinguished visitor. Currently, he is the chair
of the IEEE Technical Committee on Distributed Processing (TCDP) and
ACM distinguished speaker. He is a fellow of the IEEE.
For more information on this or any other computing topic,
please visit our Digital Library at www.computer.org/publications/dlib.

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