Compressed-Sensing-Enabled
Video Streaming for Wireless
Multimedia Sensor
Networks
ABSTRACT:
This paper presents the design of a
networked system for joint compression, rate control and error correction of
video over resource-constrained embedded devices based on the theory of
Compressed Sensing (CS). The objective of this work is to design a cross-layer
system that jointly controls the video encoding rate, the transmission rate,
and the channel coding rate to maximize the received video quality. First, compressed
sensing-based video encoding for transmission over Wireless Multimedia Sensor
Networks (WMSNs) is studied. It is shown that compressed sensing can overcome
many of the current problems of video over WMSNs, primarily encoder complexity
and low resiliency to channel errors. A rate controller is then developed with
the objective of maintaining fairness among different videos while maximizing
the received video quality. It is shown that the rate of Compressed Sensed
Video (CSV) can be predictably controlled by varying only the compressed
sensing sampling rate. It is then shown that the developed rate controller can
be interpreted as the iterative solution to a convex optimization problem
representing the optimization of the rate allocation across the network. The
error resiliency properties of compressed sensed images and videos are then
studied, and an optimal error detection and correction scheme is presented for
video transmission over lossy channels. Finally, the entire system is evaluated
through simulation and test bed evaluation. The rate controller is shown to
outperform existing TCP-friendly rate control schemes in terms of both fairness
and received video quality. The test bed results show that the rates converge
to stable values in real channels.
ARCHITECTURE:
EXISTING SYSTEM:
In existing layered
protocol stacks based on the IEEE 802.11 and 802.15.4 standards, frames are
split into multiple packets. If even a single bit is flipped due to channel
errors, after a cyclic redundancy check, the entire packet is dropped at a
final or intermediate receiver. This can cause the video decoder to be unable
to decode an independently coded (I) frame, thus leading to loss of the entire
sequence of video frames.
DISADVANTAGES OF EXISTING
SYSTEM:
Instead, ideally, when one
bit is in error, the effect on the reconstructed video should be unperceivable,
with minimal overhead. In addition, the perceived video quality should
gracefully and proportionally degrade with decreasing channel quality.
PROPOSED SYSTEM:
With the proposed
controller, nodes adapt the rate of change of their transmitted video
quality based on an estimate of the impact that a change in the transmission
rate will have on the received video quality. While the proposed method is
general, it works particularly well for security videos. In addition, all of
these techniques require that the encoder has access to the entire video
frame (or even multiple frames) before encoding the video.
ADVANTAGES OF PROPOSED
SYSTEM:
The proposed CSV encoder
is designed to: i) encode video at low complexity for the encoder; ii) take
advantage of the temporal correlation between frames.
MODULES:-
1. CS Video Encoder (CSV)
The CSV video encoder uses
compressed sensing to encode video by exploiting the spatial and temporal
redundancy within the individual frames and between adjacent frames,
respectively. _Sensing the channel : those
that have the cost of sensing channel have higher energy consumption and so
they are not suitable for WMSNs. __Using extra
packets: Using retransmission time of dropped
packets includes not only retransmission request but also transmission of
dropped packet. These methods waste a great amount of energy for congestion detection
in sensor nodes.
Low cost: Some methods do not necessitate extra cost for congestion detection.
These methods are the most suitable for congestion detection in WMSNs.
2. Rate Change
Aggressiveness Based on Video
Quality:
With the proposed
controller, nodes adapt the rate of change of their transmitted video
quality based on an estimate of the impact that a change in the transmission
rate will have on the received video quality. The rate controller Uses the
information about the estimated received video quality directly in the
rate control decision. If the sending node estimates that the received video
quality is high, and round trip time measurements indicate that current network
congestion condition would allow a rate increase, the node will increase the
rate less aggressively than a node estimating lower video quality and the same
round trip time. Conversely, if a node is sending low quality video, it will
gracefully decrease its data rate, even if the RT T indicates a congested
network. This is obtained by basing the rate control decision on the marginal
distortion factor, i.e., a measure of the effect of a rate change on video
distortion.
3. Video Transmission
Using Compressed Sensing:
We develop a video encoder
based on compressed sensing. We show that, by using the difference between the CS
Samples of two frames, we can capture and compress the frames based on the temporal
correlation at low complexity without using motion vectors.
4. Adaptive Parity-Based Transmission:
For a fixed number of bits
per frame, the perceptual quality of video streams can be further improved by
dropping error samples that would contribute to image reconstruction with
incorrect information. Which shows the reconstructed image quality both with
and without including samples containing errors? It assume that the receiver
knows which samples have errors, they demonstrate that there is a very large
possible gain in received image quality if those samples containing errors can
be removed. We studied adaptive parity with compressed sensing for image transmission,
where we showed that since the transmitted samples constitute an unstructured,
random, incoherent combination of the original image pixels, in CS, unlike
traditional wireless imaging systems, no individual sample is more important
for image reconstruction than any other sample. Instead, the number of correctly
received samples is the only main factor in determining the quality of the received
image.
SYSTEM
REQUIREMENTS:
HARDWARE
REQUIREMENTS:
•
System : Pentium IV 2.4 GHz.
•
Hard Disk :
40 GB.
•
Floppy Drive : 1.44 Mb.
•
Monitor : 15 VGA Colour.
•
Mouse :
Logitech.
•
Ram :
512 Mb.
SOFTWARE
REQUIREMENTS:
•
Operating
system : Windows XP.
•
Coding
Language
: C#.NET
REFERENCE:
Scott Pudlewski, Student Member, IEEE, Tommaso
Melodia, Member, IEEE, and Arvind Prasanna, “Compressed-Sensing-Enabled Video
Streaming for Wireless Multimedia Sensor Networks”, IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 11, NO. 6, JUNE 2012.
