I. J. Communications, Network and System Sciences, 2008, 2, 105-206
Published Online May 2008 in SciRes (http://www.SRPublishing.org/journal/ijcns/).
Copyright © 2008 SciRes. I. J. Communications, Network and System Sciences, 2008, 2, 105-206
Streaming Multimedia over Wireless Mesh Networks
David Q. LIU, Jason BAKER
Department of Computer Science
Indiana University – Purdue University Fort Wayne
Fort Wayne, IN 46805, USA
E-mail: {liud, bakejm01}@ipfw.edu
Wireless mesh network (WMN) research is an emerging field in network communications. However, WMNs
pose several difficulties in the transmission of information, especially time critical applications such as
streaming video and audio. In this paper, we provide an overview of several research papers which utilize
mesh networks for streaming multimedia. We compare the results of the research and the significance they
bring to the field of wireless mesh networks. We then provide possible directions for future research into
wireless mesh networks as they apply to streaming multimedia.
Keywords: Wireless Mesh Networks, Multimedia, Video Quality
1. Introduction
Wireless mesh network (WMN) [1] research is an
emerging field of study in network communications.
Wireless mesh networks are dynamically self-organized
and self-configured where the node in the network
automatically establishing an ad hoc network and
maintain the mesh connectivity. According to [1], there
are three classes of WMNs:
y Infrastructural Backbone WMNs: mesh routers form
an infrastructure for mesh clients to connect to them.
y Client WMNs: mesh clients constitute the actual
network to perform routing and configuration
functionalities as well as supporting end-user
y Hybrid WMNs: the mesh router infrastructure is
combined with client meshing as shown in Figure
WMNs have the following characteristics:
y Support multi-hop wireless networking
y Support for ad hoc networking
y Self-form, self-heal, and self-organize
y Support minimal mobile routers and stationary or
mobile clients
y Support both backhaul access to the Internet and
peer-to-peer communications
y Provide power efficient protocols for mesh clients
y Interoperate with existing wireless networks such as
Wi-Fi, WiMAX, Zig-Bee, and cellular networks
Recently research has bee done in the area of
multimedia stream over wireless mesh networks. A
multi-source multipath video streaming system [2] is
proposed to support concurrent video-on-demand over
wireless mesh network. Network coding [3] may be used
to increase throughput over a wireless mesh network.
Quality of Service (QoS) support is surveyed in [4].
Paper [5] proposes QoS routing in WMNs. Real-time
video stream aggregation is studied in [6]. Results from
a real testbed is reported in [7] about multimedia over
wireless mesh networks. The core questions for
multimedia streaming over wireless mesh networks is
how to establish connection, maintain transmission of
multimedia data, and achieve suitable quality across
such networks. This paper, in Section 2, presents several
techniques that will cover the topics of path
determination, adaptive quality, and cross-layer
information gathering. In Section 3 we compare the
varied techniques across several dimensions to
determine such attributes such as usefulness and
efficiency. This is to be followed by areas for future
research in Section 4. Finally, we will provide a brief
conclusion stating the resultant findings in Section 5.
2. Existing Techniques for Multimedia
Streams over Wireless Mesh Networks
178 D.Q. LIU ET AL.
Copyright © 2008 SciRes. I. J. Communications, Network and System Sciences, 2008, 2, 105-206
There are a variety of techniques for transmitting
multimedia streams across a given wireless mesh
network. However, we will limit the discussion to three
general areas: path determination techniques, adaptive
quality, and cross layer information gathering. These
techniques try to solve the problems that occur with
improving the quality and performance of multimedia
transmissions across wireless mesh networks.
2.1. Path Determination Techniques
To transmit the data from the source to the destination
requires the use of some form of path determination or
routing algorithm. One such technique utilized in [8] is
congestion-minimized routing. Congestion-minimized
routing tries, as its namesake suggests, minimizing the
congestion which is defined as the average delay per link.
This is accomplished by dividing the total transfer rate
into K sub-transfers of equal rate Rk, and assigning
each sub-transfer to a given route. They minimize the
min (1)
where ρk is the path from source to destination, Cij is the
capacity of the link from node i to node j, and F
denotes the existing network traffic plus the prior sub-
transfer rates. To find the route for the sub-flow they
make use of the capacity of the link and the current rate
of traffic across that link. The routing problem in (1) is
solved by utilizing the distributed Bellman-Ford
algorithm, where each node has to store the minimum
path cost from itself to the source and the link cost to all
the neighbors. It is stated that solving (1) using Bellman-
Ford will converge to the solution in a network of N
nodes and having diameter D within D rounds of
information exchange. After the path has been found the
destination sends a message down the reverse path to the
source, thus giving the source the routing path for route
Paper [9] demonstrates three different path selection
algorithms with varying levels of estimation utilized:
end-to-end, localized, and estimation based. For all
algorithms they assume that the topology does not
change during video transmission, and that there is no
contention for access to the medium. To accomplish this
they employ the principles of HCCA protocol as applied
to an IEEE 802.11e network. The application of this
technique allows the scheduling of multiple flows
creating an average transmission rate for each flow. The
end-to-end algorithm uses complete information of the
network so it must first generate the connectivity
structure Ρ for each node which has data to transmit. Ρ is
defined as all possible paths from source to destination
without loops. Using Ρ they find the minimum cost path
using an algorithm which exhaustively searches all
possible paths. The algorithm then finds the path which
has the smallest estimated timing requirements for
transmission. The information needed for this is
transmitted using separate logical communication links
between nodes. The next approach presented is a
localized estimation, where only the link information of
neighboring nodes is known. The rest of the path
information is estimated based upon an approximation of
several low layer data characteristics. The third
technique presented is purely estimation based and uses
an estimation technique similar to the localized version;
however, it does not even keep track of the information
on links between the nodes. Three path determination
algorithms are discussed in [10]. One is a centralized
algorithm which will not be discussed due to the fact it is
almost exactly the same as the prior end-to-end
algorithm from [9]. This is most likely due to the
majority of authors being the same and the later
publishing date of [10]. The other two are new peer-to-
peer (P2P) algorithms: distributed collaborative and
distributed non-collaborative. Both approaches assume
that there is enough available bandwidth for an overlay
layer for communication of the network conditions
between peers, and they both only run when a new flow
is admitted, an existing flow leaves, or the topology of
the network changes. The first is a collaborative
distributed algorithm, which utilizes a local greedy
approach. All nodes in the network must collectively
sort the sub-flows. Sorting is done to satisfy the utility
function for maximizing the total quality (MTQ) or the
utility function for maximizing the minimum quality
(MMQ). If they are utilizing the MTQ approach, then
the sub-flows are sorted in descending order of λx/Bx,
where λx is a flow specific parameter that depends on
video characteristics and Bx is the rate requirement for
the sub-flow. For the MMQ tactic they group the sub-
flows by the quality layer and then sort by λx/Bx. After
deciding and ordering all sub-flows the sender
determines if there is any path to the destination. If no
path exists, do not admit the video stream transmission
to send, and cancel all sub-flows that depend on the
denied sub-flow. If only one exists the transmission is
started. If multiple paths exist, the path that leads to the
smallest amount of introduced congestion is selected.
Two methods are offered for estimating congestion:
bottleneck air-time congestion and mean end-to-end air-
time congestion. Bottleneck congestion works using:
max (2)
where x
i is the i-th potential path for sub-flow x, va is
node a along the path, and a is the fraction of total
listening at va. Mean end-to-end works using the
1 (3)
Copyright © 2008 SciRes. I. J. Communications, Network and System Sciences, 2008, 2, 105-206
where |x
i| is defined as the number of nodes in the path
and the rest of the parameters are the same as in (2).
However, it defines the non-collaborative approach has
each node sorting its own flows, before scheduling any
other peers are allowed to schedule their flows to pass
through the node.
Paper [11] presents no noteworthy developments for
path determination; as it uses the UDP/IP protocol suite
for routing and transmission of data. Therefore, it can be
thought to be representative of the base line.
2.2. Adaptive Quality
Adjusting the quality of the video is a must for utilizing
available bandwidth, and for reducing congestion
throughout the network. A technique used in [8] is to
minimize the distortion of the encoded video, while
limiting the congestion introduced. They achieve this by
estimating the tradeoff between an increase in the rate
and the decrease in the distortion. The given equation
balances the rate versus the distortion
≈∆− 2
where -Ds is the distortion reduction for the stream, Rs
is the encoding rate, and θs and R0
s are determined from
trial encodings. At time intervals of size k, the source
node increases the rate allocated to the stream and
monitors the congestion versus the reduction in
distortion. If the increase in rate isn’t worth the
reduction in distortion then the rate stays where it is.
There is a pre-agreed scaling factor for the congestion.
This allows for a reduction in distortion as long as that is
less than times the increase in congestion. This
increase occurs until it reaches the optimal point, at
which point it merely responds to the congestion present
in order to raise or lower the distortion. This is
guaranteed to converge for networks with fixed rates and
link capacities.
In [9], we are presented with no adaptive quality
changes for video transmission. Once a video is desired
to be sent, the path provisioning scheme tries to find a
path which meets the necessary requirements for the
video transmission. If one can not be found, it does not
send the video stream.
In [10], they demonstrate an interesting technique
based on logical flows of data. Each sub-flow represents
a partition in the actual quality of the video. This is
represented by the equation
xxxyyy BQPQ
For (5) Qy
0 is a parameter dependent upon the video,
encoding parameters, etc. ωx is 1 for admitted sub-flows,
0 for non-admitted sub-flows, and -1 for rejected sub-
flows. λx is a parameter like Qy
0 and it is dependent on
the quality layer. Bx is the bit rate for the sub-flow.
When a sub-flow is not admitted, all “enhancement
layers” that depend upon that flow are not admitted as
well. The parameter ωx allows ease of detection for a
given non-admitted sub-flow. When a sub-flow is
rejected ωx is set to -1 recursively for all dependent
Figure 1. Wireless Mesh Network [1]
180 D.Q. LIU ET AL.
Copyright © 2008 SciRes. I. J. Communications, Network and System Sciences, 2008, 2, 105-206
ˆ (6)
where Np is the total number of aggregate flows. By
utilizing (5) MMQ can also be defined
min yy
MMQ QU Ρ= (7)
These utility functions act as constraints, which allow
the system to make quality of service decisions based
upon individual sub-flows through the previously
discussed path determination.
Paper [11] makes use of the standard MPEG video
standard. The standard makes provisions for a
quantization scale parameter (QSP), which allows the
quality of the codec to be adjusted dynamically during
the encoding process. A feedback formula is presented to
achieve adaptive quality, which is defined as:
))(:(min),,( ,]31,1[ Fjaqqq qRsqjasq
≤+=Φ= (8)
where Ra,j(q) is the rate curve for the expected number of
packets to encode for each frame type, sq is the length of
the transmission buffer before encoding the packet, and
θf is the queue length of the transmission buffer.
Equation (8) allows us to make use of the rate curve to
minimize the encoding QSP and maximize the PSNR,
while maintaining a full transmission buffer.
2.3. Cross Layer Information
Most of the techniques discussed in [8] utilize
information from the bottom three to four layers of the
OSI model. The problem is how to gather this
information so that the path determination and adaptive
quality algorithms can make use of it. Paper [8] gathers
the busy, block and idle times which are referred to as
Tbusy, Tblock, and Tidle respectively. They also keep a
running average of the video payload size for each
stream over the time period known as Bs. The rate for a
given stream on a node is:
= (9)
The estimation for bandwidth capacity is:
C (10)
Employing (9) and (10), an estimation of available
bandwidth for a given stream at the given node is
defined as:
' (11)
Estimation of the congestion increment, denoted as
Xs, at a given node can be estimated using (9) and (10)
resulting in:
which correlates the congestion increment against a
given increase in the encoding rate.
Paper [9] gathers information for each link in the
network. It utilizes the modulation, the bit error rate
(BER), and the guaranteed bandwidth denoted as m(li,j),
e(li,j), and g(li,j) respectively. The modulation is defined
by the physical medium, but there is no mention of how
the modulation is gathered. The probability of a BER is
defined as:
jivl leLe ,
.−−= (13)
where Lv is defined as the size of the MSDU in bits. This
is due to the assumption that the BER is normally and
randomly distributed. The guaranteed bandwidth is set
up so as to follow the HCCA reservation rules. These
parameters are used to estimate queuing delay for each
link as:
ljiqueue TtLdld (14)
lTtLd defines the delay for the link
between nodes i and j with MSDU of size Lu, with a
mean retransmission time at the link and max
retransmission time along the path denoted by
,iji p
lTt . Equation (14) results in the queuing delay
needed for the path determination algorithms in Section
Information is gathered at the physical and network
layers in paper [10]. The BER is also used in this paper,
and is defined as
e (15)
where µ and are constants, s is the signal to noise ratio
(SINR), and θx
a is the physical layer mode at the given
node a on stream x. This gives the expected goodput
(throughput without errors) defined as:
)()),(1( a
−= (16)
where the function εx
a is the probability of a bit error on
a packet of size Lx bits (same equation as (14), and the
function Tx
a is the physical layer transmission rate on the
physical mode θx
a. They then utilize the transmission
service interval, listening service interval, and
transmission service interval denoted as SI
t, )(RX
t, and
t respectively. The assumption is made that the
transmission service is much greater than the receiving
service, implying that congestion is due to transmissions.
Also recorded is the fraction of the listening time given
Copyright © 2008 SciRes. I. J. Communications, Network and System Sciences, 2008, 2, 105-206
to a stream for a node denoted as rx
a. Using the gathered
cross-layer information the admission of sub-flows is
decided based on the inequality:
rG <
where if this is true the sub-flow can be allowed,
otherwise it must be dropped.
3. Comparison
Given the wide variety of techniques previously
described, this section will be devoted to comparing the
overhead, benefits, and disadvantages of the techniques
as they apply to situations dealing with streaming media.
3.1. Path Determination Techniques
The variety of path determination techniques explained
in Section 2.1 have distinct advantages and
disadvantages. To more objectively discuss these
techniques they will be compared by the attributes over
the domains: information complexity, algorithmic
complexity, network overhead, congestion avoidance,
and dynamic adaptation. These domains will be assigned
ratings qualitatively from very bad, bad, average, and
good, to very good.
Information complexity covers the storage and
updateability of information, which widely varies
depending on the technique used for each algorithm.
Congestion-minimized routing is very good with
regarding to informational complexity. This is due to the
fact that it only requires estimations on the available
bandwidth and utilized bandwidth on links between
nodes as shown in (1). This information can easily be
stored with the link information, and can be
automatically updated by traffic passing through the
node. End-to-end requires perfect knowledge of the
entire network at each node and therefore has a very bad
information complexity rating. This requires a massive
amount of information to be stored at each node. Also,
when the information changes all nodes must be updated.
Localized attains a rating of good due to the fact that it
only needs to store the information associated with the
nodes one hop away in the paper, although since this
grows as it looks farther away until the complexity
reaches end-to-end. If it is just 1 hop away, the
information is easily stored as in congestion-minimized
routing. The estimation based approach receives a rating
of very good for the fact that it stores only one piece of
information regarding the link between nodes and
estimates everything off of that one piece of information.
Distributed collaborative path determination obtains an
average rating. This is due to the fact that it must store
information for each sub-flow passing through it;
including listening times, and keep this information in
sorted order to determine when to drop quality layers.
The distributed non-collaborative approach also receives
an average rating, because it must also store the same
amount of information as the non-collaborative. UDP/IP
receives a very good rating due to the fact that no
additional information has to be stored or updated.
Algorithmic complexity encompasses the run-time
and ease of implementation, which again varies widely
depending on the technique. Congestion-minimized
routing obtains a rating of good; because of the
distributed nature of the algorithm it can easily replace
the distance metric used Bellman-Ford algorithm.
However, it requires the use of an overlay network to
keep nodes current on changes in topology, and this must
be implemented to update the routes and their tables.
End-to-end achieves a rating of very bad for algorithmic
complexity due to the fact that it performs an exhaustive
search of the path space from source to destination, and
performs computations along the complete path. This
results in a triple summation along different dimensions
of activity to find the minimum path. The localized
algorithm collects an average rating. This is due to the
fact that it requires an exhaustive search of all neighbor
nodes. However, it too requires an overlay network to
convey information between the nodes, which is used to
relay the information of the path up to the current
determined node while determining which path to take.
Estimation based gets a good rating due to the fact that it
only requires the use of the overlay network to pass
messages between nodes in the wireless mesh network. It
is a simple algorithm of estimating the total path
deadline and then chooses the route that minimizes the
deadline criteria. The distributed collaborative algorithm
gets a rating of good. This is due to the fact that the path
provisioning algorithm only runs to meet significant
changes in the network. However, each time a path is
determined they have to see if the path can support the
bit flow, incurring a tremendous amount of overhead.
The ease of implementation is about the same between
this and other algorithms presented due to the overlay
layer used. The distributed collaborative algorithm
scores a good rating. This is caused by the imposed
requirement of an overlay network for notifications and
path admission. However, it does not incur the
complexity of communicating with its neighbor about
what it is doing. UDP/IP receives a very good rating
because it follows the simple pre-established algorithms
provided on all routers.
Network overhead is related to informational
complexity for any information transferred over the
network. Congestion-minimized routing obtains an
average rating for network overhead due to the fact that
again it requires an overlay network which performs
status message passing. It also requires that the Bellman-
Ford algorithm be re-run each time a path is introduced.
In addition, the algorithm takes a number of iterations of
the Bellman-Ford algorithm equal to the diameter of the
182 D.Q. LIU ET AL.
Copyright © 2008 SciRes. I. J. Communications, Network and System Sciences, 2008, 2, 105-206
graph to converge. End-to-end routing is the most costly
in terms of overhead for the network. It must obtain the
complete connectivity structure and all link information
at each node for path determination. The amount of
network overhead introduced by this is phenomenal and
grows at an astonishing rate as nodes are added to the
network, leading to its low rating of very bad. Localized
only needs to transmit information between the
neighboring nodes, so the amount of network overhead
is low and the use of the overlay network does not
significantly impact this technique. Estimation based
acquires a favorable score for network overhead due to
the fact that it transfers no information between nodes. It
utilizes only link information that is set a priori to
determine what path to take. Thus, estimation based gets
a very good rating for network overhead because there is
no overhead aside from the actual transfer. Distributed
collaborative procures a rating of average for network
overhead, because it is necessary to retrieve information
from the network to find viable paths, and admit them
into the network. This process has a high overhead in
network message passing using the proscribed overlay
network. The distributed non-collaborative technique
receives a good rating because it is similar to the
distributed collaborative in many respects, such as the
use of an overlay network and path determination sans
congestion avoidance. However, the individual nodes
don’t spend time messaging back and forth to allocate
the flows reducing the overhead. UDP/IP has no network
overhead aside from normal transmission costs; it also
has no retransmission due to failed packets.
Congestion avoidance is a necessary feature for
streaming video; if you stream video through a
congested node then you will have jittery video or even
lost streams. Congestion-minimized routing boasts a
very good rating for congestion avoidance. The
algorithm is designed to avoid the inherent problem by
estimating the network congestion and finding the path
through the network, described in (1), that minimizes
congestion. End-to-end has a side-effect of congestion
avoidance since it tries to minimize the queuing delay
along the possible paths. By thus avoiding the heavily
queued nodes, it avoids congestion. Therefore, end-to-
end receives a very good rating for congestion avoidance.
The localized estimation uses the same technique as end-
to-end; however, it does not possess perfect knowledge
of the network, and thus bad estimations may result in a
quick build up in congestion. This method will
nevertheless work for local congestion avoidance, so it
receives a rating of good. The estimation based approach
has no knowledge of network conditions and makes
estimations for all links set up and congestion from (2)
and mean end-to-end congestion from (3). The
bottleneck congestion is useful for networks with
bottlenecks, and mean end-to-end is useful in all other
cases, both techniques provide excellent feedback on the
network from the point view of reserving air space. This
leads to routing into congested areas quite easily, so the
congestion avoidance for the estimation approach has a
rating of very bad. The distributed collaborative
technique receives a rating of very good for congestion
avoidance. It gives two techniques for congestion
avoidance bottleneck. Therefore, the necessity for a
priori knowledge about bottlenecks detracts from the
possible perfect score in congestion avoidance.
Distributed non-collaborative gets a rating of very bad
for implementation due to the fact that it does not
implement any kind of congestion avoidance mechanism.
UDP/IP also gets a low rating for congestion avoidance;
by itself UDP/IP supports no inherent means for
congestion avoidance. Since no other congestion
avoidance means were discussed in [11], UDP/IP
receives a rating of very bad.
Dynamic adaptation encompasses how responsive and
accurate the algorithm works with changes to topology
and data flows. Congestion-minimized routing adjusts
the paths of video streams over time based on the
conditions in the wireless mesh network. This automatic
adjustment earns congestion-minimized routing a very
good rating for the dynamic adaptation of the
environment. End-to-end, localized and estimation based
all assume that network topology is fixed while
transmitting and that time is reserved for communication
before transmission starts. These three algorithms are
sorely lacking in the ability to respond to dynamic
changes in the wireless mesh network. They do not
reduce the current stream’s bandwidth to accommodate
new ones, and if a node becomes unresponsive during
transmission there is no recovery for this happenstance.
However, the overlay network provides a possibility of
recovery if implemented in the future. As a result we
assign all three of these techniques a rating of very bad
Distributed collaborative and non-collaborative both do
not perform dynamic adaptation of packet routing. Again,
although the algorithms used and the overlay network
potentially allow for extensibility into this area.
Therefore, distributed collaborative and non-
collaborative both receive a rating very bad also. UDP/IP
inherently routes around areas where nodes are
malfunctioning or have left the wireless mesh network;
however, it does not take into consideration data flows
already going through the network. Therefore UDP/IP
receives a rating of good.
Table 1 shows the overall associated ranks for each
technique and the critiqued areas. As can be easily seen
some path determination techniques faired better than
others. However, congestion-minimized routing,
distributed collaborative, and the standard UDP/IP suite
faired the best overall.
3.2. Adaptive Quality
To compare the adaptive quality techniques from Section
2.2, we utilize the dimensions of scalability, algorithmic
Copyright © 2008 SciRes. I. J. Communications, Network and System Sciences, 2008, 2, 105-206
Table 1. Path determination overview
Critiqued Areas
Technique Information
Minimized Routingvery good good average very good very good
End-To-End very bad very bad very bad very good very bad
Localized good average good good very bad
Estimation based very good very good very good very bad very bad
collaborative average good average very good very bad
non-collaborative good good good very bad very bad
UDP/IP very good very good very good very bad good
complexity, and smoothness. Again they will be
appraised on the same scale as used in Section 3.1.
Scalability refers to how well the technique scales in
terms of the network size and the amount of traffic
present in the wireless mesh network. The technique
presented in [8] is extremely scalable well due to the fact
that the technique only worries about its own traffic so
each transmitting node is self monitoring. There is an
overhead incurred by using the overlay network to
transmit the needed information about congestion along
the path. This is somewhere between constant and linear
in terms of the growth of the network. This can be said
because the addition of a node may or may not impact
the path taken. If the path is lengthened then, the impact
is linear; if the path stays the same, then it is constant.
Also, (4) provides a way to calculate the given decoding
distortion, which can then be utilized to optimize the
system against the increase in congestion. This leads to
even the network layer adapting to the introduction of
streams of data as described in (1). This appears to be
extremely scalable because it is also done per sending
node, with overhead in the overlay network. Therefore,
this technique receives a rating of good for scalability.
Paper [9] presents no techniques for adaptive quality and
thus obtains a rating of very bad for this section. In [10],
distributed collaborative algorithm only, the flows are
split into hierarchical layers equivalent to layers of
quality or “enhancement layers.” To make use of this
two possible utility functions are described in (6) and (7)
which do determine the MMT and MMQ respectively.
Both of these equations utilize (5), which allows the
wireless mesh network to determine the aggregate
quality of a video stream across the network. Then, when
utilizing the MMT or MMQ utility functions the system
can possibly self-optimize the flows through utilization
of the overlay network. However, the overhead incurred
on the overlay network would be quite extreme. This is
because that both utility functions require perfect
knowledge of the number of video streams through the
network and all rejected “enhancement layers” need to
be kept track of to reintroduce these video streams into
the system. This can be deduced from both utility
functions requiring perfect knowledge of the number of
video streams moving through the network to make any
decisions and from the fact that all rejected
“enhancement layers” need to be kept track of to
reintroduce them into the system. The first problem
grows tremendously with respect to the network,
whereas the second problem is only the responsibility of
the sender nodes. This leads to a rating of average for
scalability. Paper [11] presents a feedback approach
local only to the system encoding video through (8). This
leads to each individual encoder only adjusting its
encoding rate without communicating with other nodes
in the wireless mesh network. This problem scales
perfectly as there is no communication between the
nodes, therefore it receives a rating of very good.
Algorithmic complexity is defined as ease of
implementation and speed of response when performing
adaptive quality. Paper [8] has a quick initial response
due to the nature of how the congestion and distortion
converge. In the beginning, the decoding distortion
rapidly decreases as you raise the encoding rate.
Whereas, the introduced congestion in the network
increases slowly in the beginning and increases rapidly
after the initial low rates. Thus, the network quickly
converges upon the goal encoding rate given the
maximal network congestion. Ease of implementation is
again a problem with the techniques described in [8]. For
the algorithm to efficiently and quickly converge, the
overlay network must be implemented and quickly pass
the requisite knowledge to the appropriate nodes. This
information passing overlay network is a significant
burden to overcome, which is why it receives a rating of
good for algorithmic complexity. Again, [9] obtains a
meager rating of very bad for not having implemented
any kind of adaptive quality. Paper [10], with respect to
the collaborative approach, has a quick response due to
several key features dealing from queue admittance
discussed in path determination to the actual use of
184 D.Q. LIU ET AL.
Copyright © 2008 SciRes. I. J. Communications, Network and System Sciences, 2008, 2, 105-206
MMQ and MTQ to ensure quality of service. This quick
convergence is due to the fact that for admittance into
the network, a given sub-flow is ordered by the benefit
to the aggregate video stream. Thus, all base flows will
be added; then, “enhancement layers” will be added by
order of contribution. However, this is detracted by the
need for an overlay network to enforce the utility
functions, ensure admittance of new sub-flows, and
update all nodes in the wireless mesh network. These
detractions result in a rating of good for algorithmic
complexity. Paper [11] presents an adaptive quality
technique which is apparently simplistic to implement
and extremely responsive to the criteria presented. It is
simple to implement because the algorithm runs at each
sender and encodes at the given quality rate based upon
the current transmission buffer capacity as determined by
(8). It responds instantaneously to any change in the
buffer. These attributes give this technique a rating of
very good.
Before we can discuss smoothness, it must be defined
with respect to streaming media over wireless mesh
networks. Smoothness will be defined as the continuity
of the quality as it changes over time. In [8] the
smoothness is somewhat ideal. This can be attributed to
the small changes in (4). The congestion is only
incremented by a given amount for each rate increase.
This algorithm works on k time steps, such that it only
increases or decreases the rate over time based on the
current congestion in the network. This leads to a very
smooth curve of quality for small k and a very
discontinuous curve for large k. For most applications,
though, it would be safe to assume that k is much smaller
than 2 seconds. Therefore, it is safe to say that a rating of
very good is very accurate and deserved for this
technique. Again, [9] receives a rating of very bad due to
not implementing any techniques. For [10] — we once
more only consider the distributed collaborative — it
appears to have an ordered but non-uniform smoothness.
This can be said because the system implicitly orders the
quality layers by contribution to the quality dependent
upon the chosen utility function. Ergo, the quality is as
smooth as the quality layer added or removed based
upon network conditions. The problem associated with
this is the varying delta for the improvement in quality.
For more important base flows, the delta is much higher
than for less important “enhancement layer” flows. This
is still a good technique; it’s just more prone to non-
uniform increases in visual quality. Thus it has a
resultant rating of very good. Paper [11] implements the
feedback formula in (8). However, this leads to problems
in quality where the algorithm tries to ensure the
transmission buffer capacity is constant. By ensuring the
buffer capacity is constant, the quality of the frame may
change suddenly if the buffer rapidly increases and
decreases in size radically in an alternating fashion. This
will leads to an oscillating digital waveform where the
resulting video will have extremely high quality images
and then drop to low quality images. Because of this
severe deficiency the algorithm scores a rating of bad.
Table 2 presents the final results for the papers
adaptive techniques versus the critiqued areas. Most of
the adaptive quality techniques are of above average
quality overall.
Table 2. Adaptive quality
Critiqued Areas
Technique Scalability Algorithmic
Complexity Smoothness
Media-aware [1] good good very good
Cross-layer [9] very bad very bad very bad
Resource-Ex [10] average good very good
Multipath-Rt [11] very good very good average
3.3. Cross Layer Information Gathering
The criteria used to compare the cross layer information
gather techniques are estimation accuracy and ease of
Estimation accuracy involves the underlying
assumptions and the accuracy of the actual estimation
formula. Paper [8] gathers information from nodes and
congestion. Equation (9) utilizes the busy, block, and
idle times of the wireless card along with the payload
size of the stream to calculate estimation on the rate for a
given stream. This seems like a reasonable estimation
due to the fact that it represents the amount of actual
transmitted data over the entire time it took to transmit
said data. The equations (10) and (11) deal with capacity
of actual nodes. For (10), we assume that the estimated
capacity is extremely accurate because the blocking time
isn’t normally large, and the busy time represents the
time to send. This is a fairly accurate estimate on the
capacity, as long as the packet sent is of an average size.
Equation (12) provides a direct correlation between rate
and congestion. This common observation is that an
increase in rate leads to an increase in congestion. This is
a very apt model for congestion increments. If the
amount of expected bandwidth left is minuscule then the
congestion blows up quickly for even small rate
increments. However, if a plethora of bandwidth is left
which results in a much smaller increase in congestion.
The only possible problem in this formula is that the sum
of the stream rates is larger than the expected bandwidth,
which is impossible due to the lack of idle time being
present in the estimated capacity. The validity of
assumptions and accurate estimates results in a rating of
very good. Paper [9] gathers information about low level
transmission and buffer issues with respect to cross layer
information gathering. They utilize the BER and the
packet size in (13) to determine the probability of an
error happening during transmission across a given link.
This is an accurate assessment of an error happening on
the link for a packet of size Lv bits. Due to the equation
Copyright © 2008 SciRes. I. J. Communications, Network and System Sciences, 2008, 2, 105-206
calculating the probability of an error not occurring for
one bit, and taking that to a power equal to the number
of bits, this is not a problem. This gives the probability
of an error not occurring for the transmitted data. The
assumption they made was that the BER was normally
and randomly distributed which is a fair assumption for
normal network transactions. Using (13) they derive (14)
which represents expected queuing delay on a given link.
This formula sums all the queuing delays for all MSDU
passing through the given link, which gives an
approximate estimation of the queuing delay. However,
this assumes that the delay per packet is accurately
calculated. Given the amount of resources poured into
devising this calculation in the paper, we assume it is
valid without further verification. Therefore, this paper
obtains a rating of very good for estimation accuracy
also. Paper [10] gathers information on expected
throughput and the BER. Equation (15) calculates the
expected error rate given the physical layer mode. Again,
this equation will have to be assumed correct due to the
lack of resources for further verification. Equation (16)
is much more feasible in its intentions. It calculates the
expected valid throughput for the packet size, and
multiplies this against the expected physical rate on the
given physical mode. The assumptions that the error rate
is accurate make this a good estimation of the available
goodput. However, this is detracted by the fact that it
does not subtract bandwidth for retransmissions caused
by errors in the estimation. As for (17), this is a simple
admission metric which accurately represents the ability
to transmit a given packet. This assumes that goodput,
service intervals, and the listening time given to the
stream are known values. Due to the high number of
assumptions and some flaws in the estimation we would
assign this paper a rating of good.
Ease of implementation covers the ease of gathering
this information and the availability of the actual
information using modern systems. For the information
that [8] gathers it is safe to say that not all of it is
accessible by hardware. It is challenging to believe that
the busy, idle, and wait time for each packet is accessible
via a hardware interface. If the hardware does not
support retrieval of this information, it has to be
estimated from a custom device driver sitting on top.
This device driver could perform a timing operation
when called to send a message across the network
interface, which can then be stored for future retrieval.
The packet size is obviously easily calculated. Thus, the
cross layer information for this paper is easily
implemented. Due to the one major deficiency, the score
obtained is only a rating good. Paper [9] has issues about
where the majority of the cross layer information comes
from. The modulation for the transmission at the
physical layer is needed for many calculations. However,
where and how the modulation is obtained is never
discussed. This is a serious shortcoming for the
algorithm. The error rate and throughput are easily
calculated and implemented. Thus, I would say there are
not any issues with the rest of the low level gathered
statistics. Therefore, this paper obtains a rating of good.
The last information gathered is from [10]. Here they
gather the BER from the physical layer mode. Again
there is no mention of how the physical layer mode is
determined as in [9]. If this mode is determined a priori
then all nodes need to be set up in advance. Otherwise,
the system needs to be able to identify the physical layer
mode automatically and adjust accordingly. The MSDU
size is easily calculated from known information at the
application level. Other low level information like the
length of the reservation time is easily transmitted across
the overlay network and must already be known to
actually send the video. Thus, we assign a rating of good
also for the same reasons as the other sections.
Table 3 provides a quick overview of how cross layer
information gathering faired for each paper. All papers
essentially estimate accurate low level information or
gather it directly. There do not appear to be any major
problems with how the information was gathered or
Table 3. Cross layer information gathering
Critiqued Areas
Technique Estimation
Ease of
Media-aware [1] very good good
Cross-layer [9] very good good
Resource-Ex [10]good good
Multipath-Rt [11]N/A N/A
4. Possible Future Research
There are a variety of areas for potential research for
streaming media over wireless mesh networks, due to the
new and their emerging nature and the problems they
4.1. Dynamically Adapted Path Determination
From the path determination techniques presented in this
paper it is clear that dynamically adapted routing
algorithms need to be researched for wireless mesh
networks. The majority of the presented algorithms had
problems when faced with working on topologies that
4.2. Congestion Avoidance
Congestion in a wireless mesh network leads to dropped
packets, reduced throughput, and dropped video streams.
To avoid this cross layer techniques need to implement
congestion avoidance. Once implemented, this will help
increase the total network utility and save on wasted
186 D.Q. LIU ET AL.
Copyright © 2008 SciRes. I. J. Communications, Network and System Sciences, 2008, 2, 105-206
4.3. Adaptive Quality to Algorithms
Adaptive quality is requisite to meet the demands of the
changing network. Without adjustments to quality, video
transmissions may be jittery or even dropped. All that is
needed is a dynamic adjustment of the video quality with
respect to the state of the network. Several algorithms
presented in this paper are good starting points for
continuing adaptive quality algorithms.
4.4. Network Overlays
Several algorithms utilized network overlays in this
paper. However, the overhead incurred by the network
overlays is extreme in some cases. Optimizations need to
be made to existing algorithms, or overlay networks need
to be redesigned to reduce the overhead incurred by
overlay networks.
4.5. Information Reuse
Cross-Layer optimized methods were the bulk of the
presentation in this paper. A majority of the papers here
only used the information gathered for one very specific
purpose and not as many ways as could possibly be done.
4.6. Expansion of Described Algorithms
The current algorithms presented in these papers could
be improved in a variety of ways such as congestion
avoidance. A majority of the papers had the capability
for congestion avoidance and simply not yet
implemented it.
5. Conclusions
This paper compared five papers which streamed video
across wireless mesh networks. All papers were
decomposed into sections dealing with path
determination, adaptive quality, and cross layer
information gathering. Commonly an overlay network
was used to ensure transmission quality, and route
information to nodes in the network. Path determination
was lead by the newer congestion-minimized, localized,
and distributed collaborative algorithms on average.
Although, these techniques are harder to implement and
have a higher overhead they are more flexible in the long
run. The congestion-minimized routing approach is a
good example which is more flexible than UDP/IP,
especially in areas of congestion avoidance. Adaptive
quality — which is important for the fact that quality
must be dynamically adjusted during transmission to
deal with the state of the network — was planned into
the algorithms in some way. Wireless mesh networks are
new and emerging field of study, but this entails that
strong research needs to be focused on this area. If this
is done it will allow for new robust algorithms for
transmitting large amounts of data wirelessly
independent of any centralized control.
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