40120140504014 2

International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 4, April (2014), pp. 103-115 © IAEME
103
VAQMAC: VIDEO AWARE QoS MAC PROTOCOL FOR WIRELESS VIDEO
SENSOR NETWORKS
Vijay Ukani
1
, Tanish Zaveri
2
, Sameer Kapadia
3
1
Computer Science & Engineering Department, Institute of Technology, Nirma University,
Ahmedabad - 382 481, Gujarat, India.
2
Electronics and Communication Engineering Department, Institute of Technology, Nirma
University, Ahmedabad - 382 481, Gujarat, India.
3
IP.Access, Pune, Maharashtra, India.
ABSTRACT
Multimedia applications over wireless sensor networks are emerging rapidly. There is an
increasing interest in the research community to design and develop critical services which require
video monitoring or emergency voice calls over WSNs. On the other hand, storage, processing and
bandwidth limitations of sensor nodes make multimedia data transmission a challenging issue for
WSNs. In such context, multimedia coding and efficient exploration of the application contents at
other layer of the stack may enhance the efficiency of WMSN applications. IEEE 802.11e is a variant
of IEEE family of LAN standard which is most suited for QoS hungry multimedia traffic in modern
Wireless Local Area Networks. However, it fairs poorly capable of handling multimedia flows
efficiently in congested networks. The main reason poor performance is due to static resource
allocation specified in IEEE 802.11e. The default TXOP limit values and priority assignment to I, B,
P frames werestatically assigned. In this work, video aware cross-layer mechanism is proposed.Under
the modified EDCA that permits dynamic allocation of TXOP limit values and change the priority of
frames during congestion. The proposed protocol also copes with high load situations by selectively
prioritizing and mapping multimedia frames to appropriate Access Categories (AC’s).
Key words: MAC Protocol, Quality of Service, Video Transmission, Wireless Sensor Network,
Cross Layer, Video- Aware Transmission.
INTERNATIONAL JOURNAL OF ELECTRONICS AND
COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET)
ISSN 0976 – 6464(Print)
ISSN 0976 – 6472(Online)
Volume 5, Issue 4, April (2014), pp. 103-115
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1. INTRODUCTION
Wireless Sensor Network (WSN) is a collection of resource constrained tiny devices capable
of measuring and retrieving important phenomenon. Applications of WSN are ever increasing due to
its capabilities in monitoring applications like traffic monitoring, industrial process monitoring,
habitat monitoring etc. Wireless Multimedia sensor network is a network of miniature sensor devices
that consist of CMOS cameras and microphones possibly along with scalar sensors that retrieve
multimedia content such as audio, video streams, still images and scalar sensor data from the
environment[1][23]. Traditional WSN equipped with scalar sensors like temperature, pressure,
humidity, electro-magnetic has high false alarm rate in detecting events. Multimedia sensor networks
complements the scalar WSN by capturing every minute detail about the phenomena and thus
reducing the error rate in event detection/classification thus improving applications like surveillance
and disaster monitoring. The coverage model of video sensor nodes is quite different than scalar
sensors. Scalar sensors tend to have omnidirectional coverage while video sensors have directional
coverage. The coverage of video nodes is modeled as Field of View (FoV). Due to sector like
coverage of the video sensor, events occurring behind the node is not detected. So a greater
deployment density is expected in the deployment of video sensor network.
Due to limited availability of bandwidth (up to 250 Kbps in MICAZ motes [MICAZ]) in WSN, the
visual data sensed by source nodes have to be compressed and transmitted to the sink over the sensor
network [13]. Also variable environmental conditions leads to high error rates. Looking into limited
bandwidth availability and high error rate, some multimedia coding providing error resilience is
needed. This may sustain the minimum acceptable end-to-end quality of the application, even when
some packets are lost while transmitted over error-prone wireless links. Most of the video encoding
techniques creates packets of varying importance. The important packet should be treated with high
priority at all levels in the communication stack.
The IEEE 802.11e MAC protocol is a member of IEEE family of LAN standard [15]. IEEE
has suggested several improvements over standard IEEE 802.11 for enhancing the data rate of
transmission. However, due to equal treatment to each packets by DCF in IEEE802.11, enhancement
in data transmission rates may be insufficient approach for multimedia applications. DCF
implements single queue model and does not support traffic prioritization. This deficiencies of
802.11 lead to development of a new QoS-aware MAC layer protocol capable of assigning different
priorities to different packets named as IEEE802.11e [15]. The prioritization offered by IEEE802.11e
is static in nature i.e. a specific type of packet is always mapped to a particular level of queue.
Sometimes in multimedia transmission, based on current occupancies in the queue, dynamic mapping
of packets to different queue is needed. As adaptive approach to mapping video packets to
appropriate queue is proposed and evaluated in this work.
2. 802.11E
As opposed to single queue model of IEEE 802.11, the new 802.11e proposed to have
multiple queues with varying priority. It defined four Access Categories (AC) with each with
different precedence. The packets were mapped to either of these ACs using mapping rules. The
EDCA mechanism is a modified version of standard DCF designed to support differentiated and
distributed channel access [19][22].

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Figure 1: Multiple backoff with different priorities
802.11e provides service differentiation by providing four different levels of priorities from 0
to 3, with 3 being the highest priority [16][17]. It implements four ACs corresponding to 4 priority
level. A frame is marked by upper layer into a priority class before being sent to MAC layer for
forwarding. 802.11 has designated the four ACs for specific type of traffic. These are: AC(3) for
voice traffic, AC(2) for video traffic, AC(1) for best effort traffic and AC(0) for background
traffic[5]. Each AC works as an independent back-off entity, and the differentiation among ACs is
provided by specific parameters. The differentiating parameter are minimum contention window,
maximum contention window and Arbitration Inter Frame Space (AIFS) as shown in figure 1.The
parameters are shown on table 2. Each AC is variation of DCF called EDCAF and each frame is
mapped to appropriate AC according to its priority value as shown in Table I.
Table 1: Priority to access category mappings
Traffic
Type
Priority Access Category (AC)
Voice
Background
Traffic
3 3
Video 2 2
Best Effort 1 1
Background 0 0
Each MAC frame is delivered through multiple backoff instances within one station, each
parameterized with its specific parameters. During the contention period, each AC contends for a
transmission opportunity (TXOP) and independently starts a backoff after detecting the channel
being idle for an AIFS[18]. The AIFS has at least DIFS duration, and can be enlarged individually

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for each AC. After waiting for AIFS, each backoff sets a counter to a random number drawn from the
interval [1, CW+1]. The minimum and maximum value for CW based on AC as shown in Table 2.
Table 2: Parameters of EDCA for different access categories
Parameters AC(0) AC(1) AC(2) AC(3)
AIFS 7 3 2 2
CW_MIN 31 31 15 7
CW_MAX 1023 1023 31 15
TXOPLIMIT 0 0 0.006016 0.003264
In case of voice transmission, smaller packets are transmitted. Each packet transmission
needs to contend for channel, leading to delay in transmission. To allow such stations to transmit
multiple packets without contending for each packet, 802.11e provides a station the right to transmit
for time indicated by Transmission Opportunity (TXOP) leading to Contention Free Burst (CFB)
transmission. The default values of AIFS, CW and TXOPLIMIT is specified by IEEE standard. The
CFB is enabled for multimedia ACs(AC(3) and AC(2)) but not available for other ACs.
Authors in [14] developed a dynamic TXOP limit adaptation scheme based on the average number of
packets allocated in QAP queues in an attempt to alleviate the downlink/uplink problem. The values
of TXOP is adaptively computed by each AC at the beginning of each beacon interval.A distributed
enhanced TXOP (ETXOP) limit adjustment mechanism was proposed in [11] which computes new
value whenever a AC(2) or AC(3) i.e video or audio AC’s wins the contention. This mechanism of
computing TXOP is based on the priority of ACs. In both cases, the TXOP value is computed based
on the current occupancy in the AC queue.
In [12], Ksentini et al proposed a QoS cross-layer mechanism involving application and
MAC layer cross communication for improving H.264 video transmission over IEEE 802.11e
networks. The approach relies on data partitioning technique at the application layer. Thepackets are
prioritized by the encoder and are mapped to appropriate ACs based to their importance. The
multimedia traffic is mapped to AC(3), AC(2) and AC(1) while AC(0) is used for all other traffic.
The retry count parameter is used to protect various frames unequally. High priority frames have
higher retry count compared to low priority frames. A similar mechanism for MPEG-4 video
involving multilevelqueue by assigning I-frames to AC(3), P-frames to AC(2), B-frames to AC(1)
and non-video frames to AC(0) was proposed in [6].An algorithm for dynamically mapping video
frames to appropriate ACs was proposed in [2]. The packets were differentiated and higher priorities
are given to forward packets. When queue length of AC(2) fills up, forward packets are remapped to
lower access category AC(1).
However with multiple queues also it has been proved that for multimedia traffic under
congestion condition, it deemed insufficient [3][4][10]. Thus, it is necessary to provide multimedia
QoS provisioning by following a crosslayer design that combines the multimedia traffic
characteristics along with strategies offered by lower layer. Also other concepts like TXOPLIMIT
adaptation and acknowledgement policy can be exploited.

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3. MULTIMEDIA CHARACTERISTICS AND EVALUATION
Any application requiring streaming of multimedia data is always demanding than any other
data sensing applications in wireless sensor networks. This is due to the extensive requirements for
complex audio/video encoding. Due to limited resource availability on sensor nodes, it demands
video coding which is simple, low data rate, error resilient and energy efficient as faras possible. In
order to achieve good quality transmission at lower transmission rates, codecs should reduce the total
data using some compression technique. The compression tends to be time consuming and processor
hungry and at the same time usually lead to some information loss.
Several encoding video techniques were proposed in literature for resource constrained WSN
including distributed source coding and Multi Descriptive Coding (MDC)[1]. As of now, most of
current Video sensor network platforms and prototypes focus on well-known intra-frame
compression algorithms and rely on in-network processing for data reduction. MPEG is one of the
most widely used video encoding technique [20]. MPEG encoder the raw video in three types of
frames viz. intra-coded frame I-frame, predictive coded frame P-frame and bidirectional coded B-
frame. The I-frames are intra coded, i.e. they are coded using spatial compression and can be
reconstructed without any reference to other frames. The P-frames are inter-coded, uses spatial and
temporal compression and are forward predicted from the last I-frame or P-frame. Reconstruction of
a P-frame needs data of another frame (I or P). The B-frames like P-frames are inter-coded but are
both, forward and backward predicted from the last/next I-frame or P-frame, i.e. there are two other
frames necessary to reconstruct them.
In a typical MPEG encoder I, P and B frames are organized into Group of Pictures (GoP).
The sequence of frames in a GoP that make up a GoP is called GoP sequence. A GoP contains
sequence of frames starting from a I-frame to next I-frame (non inclusive).A GoP sequence is
characterized by the number of frames, N in a GoP and the number of B-frames, M between
successive P-frames [8][20].For e.g. G9B2 has 9 frames in GoP with 2 B-frames between successive
P-frames leading to structure like I B B P B B P B B I.
I-frames are intra-coded frame exploiting only spatial redundancy using JPEG encoding
hence they can be decoded without depending on any other frame. Other frames P and B exploit
temporal redundancy and are inter-coded with reference to the preceding I or P-frame using motion
estimation and compensation. As temporal redundancy is exploited by P and B frame, they tend to
achieve higher compression ratio but at the same time they are dependent on other frames for
decoding [9]. As I-frame is the only intra-coded frame, loss of I-frame will leave a GoP completely
undecodable. The next important frame is P-frame as subsequent P and B-frames are dependent on it.
Quality loss incurred due to loss of P frame depends on its relative position in the GoP.
An MPEG decoder uses the I-frame as the reference frame for remaining frames in a GoP.
Any Impairment in I-frame will lead to artifacts that propagates through that GoP, and the video will
recover only in the next GoP when an unimpaired I-frame is received. Due to large size of I-frame, it
may need to be transmitted in multiple IP packets. Loss of apacket at the beginning of the I-frame
which carries frame header information will lead topixelization that will continue through the GoP.
A Packet loss bearing I-frame but not carrying header information will result in slice errors that will
continue through the GoP. Loss of a P-frame also has some effect in decoding quality. A GoP
contains many P-frames. The effect of P-frame loss depends on the position of lost frame in the GoP.
P-frames in initial part of GoP create alonger impairment because many subsequent frames are used
as reference frame by following P or B-frames. Alsolike an I-frame loss, for video sequence with
higher motion, loss of P-frame also results in greaterthe pixelization. B-frames are considered as low
significant frames in the GoP as they are not referencedby other frames and loss of B-frame do not
have much effect on video quality.

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Several experiments were carried out to study the effect of frame loss on quality of reception.
The quality of reception was measured with video quality measures like Peak Signal to Noise Ratio
(PSNR), Structural Similarity (SSIM), and Video Quality Measure (VQM). The experiment was
performed on foreman QCIF video sequence with 176x144 resolution with 300 frames and frame
rate of 30 fps. The GoP length was set to 30. The plots in Figure 2 shows quality of individual frames
of a GoP measured with PSNR, SSIM, and VQM. The plots shows effect of losing the only I-frame,
first P-frame and 11th P-frame.
Figure 2: Effect of frame loss on received video quality
The PSNR metric compares the quality of the video received by the user with the original
video by frame by frame comparison. ThePSNR is expressed in dB (decibels). For a video to be
considered as good quality, itshould have an average PSNR of at least 30dB. The PSNR values
typically lies between 30 dB to 50 dB, with higher values considered better than lower ones.The
SSIM measures the structural distortion of the video, which tries to obtain a better correlation with
the user's subjective impression. The value of SSIM varies between 0 and 1 with values closer to 1
indicates better video quality. The VQM metric is a measurement of the "perception damage" the

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video has experienced. It works on the Human Visual System (HVS) characteristics, including
distinct metric factors such as blurring, noise, color distortion and distortion blocks. VQM results
varies from 1 to 5, where values closer to 1 indicates better video quality.
4. VIDEO-AWARE QoS MAC (VAQMAC)
A cross-layer video aware MAC protocol is proposed in this work, whichintegrates the video
information with MAC layer strategies. Considering the characteristics of WSN, where an event is
detected, captured and reported by large number of nodes, congestion conditions are frequently
possible. VAQMAC is an integrated QoS provisioning mechanism that combines application and
MAC layer features to achieve its purpose.
As per IEEE802.11, all the video frames are to be mapped to AC(2). In VAQMAC, the video
frames are mapped to AC(2) and AC(3). It maps I and P frames to AC(3) as I and P video frames are
considered as the most important video frames, they must be transmitted with the highest possible
priority[7]. B frames being the lower priority video frames are mapped to AC(2). Data traffic is
mapped to AC(1). Once contention is won by an AC, appropriate strategy is to be selected for frame
transmission. As AC(3) contains I and P frames which are larger in size, it might quickly run out of
buffer in case of congestion. It is proposed to adapt TXOPLIMIT to transmit more frames and clear
the buffer as early as possible.
Now to overcomethe congestion situation, queue occupancy is monitored constantly. Once
the queue occupancy of AC(3) crosses the threshold, which is taken as 70% of the initial queue
length, it means network traffic is high in AC(3) and action to alleviate this congestion is to be taken.
Threeapproaches are used in VAQMAC, first the incoming P frames are redirected mapped to AC(2)
and second TXOPLIMIT is increased by a factor of αwhich is a function of current queue
occupancy(higher the queue occupancy, higher the value of α) and third the traffic is delayed by 0.2
unit of time. Once the queue length reaches to its stable condition i.e. less than or equal to threshold
value, incoming P frames are again mapped to AC(3).This will lead to fluctuation in the mapping
process. So RED like thresholds are used with a lower threshold and an upper threshold. Upon
crossing the upper threshold, the P-frames are mapped to AC(2) until the queue occupancy falls
below lower threshold. When queue occupancy of AC(2) is above upper threshold value, three
approaches are used, first the incoming B frames are mapped to AC(1), and second TXOPLIMIT is
increased by a factor of αwhich is a function of current queue occupancy. Increase in TXOPLIMIT is
needed as P-frames were redirected to AC(2) and due its large number and size, it tends to get full in
short time.The third approach is to delay the traffic by 0.2 unit of time. However when queue length
reaches lower threshold value, incoming B-frames are again mapped to AC(2), TXOPLIMIT will be
restored to original value as earlier.
In existing 802.11e EDCA the TXOP limit parameter is static and different for all AC’s as
shown in Table 2 but in VAQMAC, the TXOP limit parameter is tuned according to the queue length
of respective AC’s. The proposed approach is demonstrated in Figure 3 and 4.
5. SIMULATION SETUP
The VAQMAC was simulated in ns-2 with WSN support being provided by nrlsensorsim.
IEEE 802.11e EDCA patch is used for 802.11e support. The network consisted of 51 nodes. Sources
generates video traffic using evalvid[21] and CBR traffic. Initial energy of each node is set to 20
joules. One of the node is sink node. The number of sources generating traffic is varied to induce
congestion situation.

International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976
6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 4, April (2014), pp.
Figure
6. RESULT ANALYSIS
The proposed protocol, VAQMAC is compared with standard 802.11e
Packet Delivery Fraction (PDF), End
Standard deviation in PSNR and PSNR (
shows a frame of the video transmitted and corresponding frame reconstructed at the sink. Figure
to 10 depicts the results obtained from simulations for the average
average PDF (for video+CBR traffic
deviation.
6472(Online), Volume 5, Issue 4, April (2014), pp. 103-115 © IAEME
110
Figure 3: VAQMAC approach
The proposed protocol, VAQMAC is compared with standard 802.11e EDCA in terms of
, End-to-end delay as network quality of service metrics and
PSNR (Peak signal to noise ratio) asvideo quality metrics
shows a frame of the video transmitted and corresponding frame reconstructed at the sink. Figure
depicts the results obtained from simulations for the average PDF (for video
traffic), average PSNR, average End-to-end delay and
© IAEME
EDCA in terms of
as network quality of service metrics and
quality metrics. Figure 5
shows a frame of the video transmitted and corresponding frame reconstructed at the sink. Figure 6
video traffic only),
end delay and PSNR standard

Figure 4 shows difference in quality of video frames of transmitted
(A) Transmitted Video frame
Figure
PDFResults (Video traffic only)
Figure 5 shows that the packet delivery ratio of VAQMAC is better that 802.11e EDCA when
only video data is transmitted.Initially with less number of traffic sources, the PDF is almost similar
but as the number of traffic sources are increased, the packets drops rapidly
overflow of queues at intermediate nodes whereas
TXOP limit of each queue and mapping the frames to appropriate AC’s
Figure 5: Avg p
PDF Results(Mixed traffic)
As shown in Figure 6, the avg. PDF
existing EDCA, as the number of traffic sources are increased, the packets drops rapidly whereas
VAQMAC can handle more traffic and shows improvement in packet drops.
111
shows difference in quality of video frames of transmitted video and reconstructed
frame (B) Reconstructed Video frame
Figure 4: Reconstructed Video
shows that the packet delivery ratio of VAQMAC is better that 802.11e EDCA when
as the number of traffic sources are increased, the packets drops rapidly in EDCA
overflow of queues at intermediate nodes whereas VAQMAC can handle more traffic by tuning the
TXOP limit of each queue and mapping the frames to appropriate AC’s.
vg packet delivery fraction with 51 nodes
As shown in Figure 6, the avg. PDF for video and CBR traffic shows the similar results for
can handle more traffic and shows improvement in packet drops.
© IAEME
constructed video.
Reconstructed Video frame
shows that the packet delivery ratio of VAQMAC is better that 802.11e EDCA when
in EDCA due to
can handle more traffic by tuning the
for video and CBR traffic shows the similar results for

Figure 6: A
End-to-end delay
The avg. end-to-end delay in Figure
delay of frames is more as compare to
12 are experiencing more delay due to congestion at intermediate nodes whereas in
to dynamic mapping of frames during congestion, the traffic sources experience lesser delay.
Figure 7: Average end
Peak Signal to Noise Ratio (PSNR)
The quality of reception is measured in terms of PSNR. The PSNR of a video is computed by
comparing individual frames of transmitted and received video. The avg. PSNR is computed for
different video sources for EDCA and VAQMAC and is demonstrated in Figure
values are above 25 dB, whileexisting EDCA
sensor nodes. PSNR values above 30 dB
dB for a video isconsidered as poor quality video.
112
Avg PDFfor mixed traffic with 51 nodes
in Figure 7shows that for existing EDCA the average end
delay of frames is more as compare to VAQMAC. As it can be observed, the traffic sources 4, 7 and
12 are experiencing more delay due to congestion at intermediate nodes whereas in
Average end-to-end delay with 51 nodes
Peak Signal to Noise Ratio (PSNR)
CA and VAQMAC and is demonstrated in Figure 8. Majority of PSNR
above 25 dB, whileexisting EDCA suffers from lower PSNR values due to
alues above 30 dB normally indicates a good video quality, anything
for a video isconsidered as poor quality video. Obviously, VAQMACperforms better than
© IAEME
shows that for existing EDCA the average end-to-end
traffic sources 4, 7 and
12 are experiencing more delay due to congestion at intermediate nodes whereas in VAQMAC, due
Majority of PSNR
suffers from lower PSNR values due to congestion at
anything below 25
performs better than

existing EDCA due to the prioritization of
significant frames to appropriate AC’s during congestion.
Figure 9, also shows that for existing EDCA the deviation in received video from original video is
more as compare to VAQMAC. More the deviation, lesser the quality of received video.
Figure
Figure
7. CONCLUSION
VAQMAC exploits the multimedia packet semantics to selectively prioritize and protect
important frames. It also adaptively assigns network resources to frames based on state of the
network. The TXOPLIMIT adaptation results in transmitting the queued packets as a burst leading to
decrease in end-to-end delay of the important packets. Also mapping of frames to different ACs
based on current occupancy of the queue improves the performance of video transmissi
wireless sensor network. By integrating application layer semantics with MAC layer strategies,
VAQMAC has proven to be extremely beneficial in terms of QoS evaluation metrics.
113
prioritization of important video frames and dynamic mapping of least
significant frames to appropriate AC’s during congestion.The standard deviation of PSNR
shows that for existing EDCA the deviation in received video from original video is
ore the deviation, lesser the quality of received video.
Figure 8: Avg PSNRwith 51 nodes
Figure 9: Standard deviation in PSNR
adaptation results in transmitting the queued packets as a burst leading to
end delay of the important packets. Also mapping of frames to different ACs
based on current occupancy of the queue improves the performance of video transmissi
. By integrating application layer semantics with MAC layer strategies,
© IAEME
video frames and dynamic mapping of least
tandard deviation of PSNR as in
shows that for existing EDCA the deviation in received video from original video is
ore the deviation, lesser the quality of received video.
adaptation results in transmitting the queued packets as a burst leading to
end delay of the important packets. Also mapping of frames to different ACs
based on current occupancy of the queue improves the performance of video transmission over
. By integrating application layer semantics with MAC layer strategies,

114
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