Transcript Mobile Television

Mobile Television
Valluri Kalyan
April 26
Topics Covered
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Introduction
DVB-H Standard
Issues
Proposed Solutions
Conclusion
References
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TheDVB-H(DigitalVideoBroadcasting for
handheld) is an extension of DVB-T. Delivery of
rich multimedia content was missing in DVB-T
and DVB-H technology enhanced DVB-T by
adding the feature of multimedia content.
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The physical layer has four extensions to the
existing DVB-T physical layer. First, the bits
in transmitter parameter signaling (TPS) have
been upgraded to include two additional bits t
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Second, a new 4K mode orthogonal
frequency division multiplexing (OFDM) mode
is adopted for trading off mobility and singlefrequency network (SFN) cell size,
Third, a new way of using the symbol
interleaver of DVB-T has been defined
Finally, the addition to DVB-T physical layer
is the 5-MHz channel bandwidth
A Conceptual DVB-H Receiver
Issues
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Resolution Requirements
Voltage Scaling
Prioritizing Data In MPEG-4
Resolution Requirements
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The Major problem for Mobile Television
Devices such as handheld PDA’s ,Phone etc
is the extent unto which the image can be
reduced, without affecting the perceived
quality of the image.
By doing so bandwidth also can be saved to
a greater extent
There is a tradeoff between screen size and
portability of the device
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Reducing the image size can have two
opposing effects
Firstly a smaller image resolution will facilitate
bitrate savings as there is less information to
be coded.
Hence for a fixed encoding bitrate,it is
possible that the percieved quality is
increased as bandwidth budget per pixel is
increased when the image resolution is
reduced
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A negative impact of reducing image size is
that we have only a fewer pixels to represent
the information to the user.
This creates problem in content types such
as sports.
We will have only fewer pixels available to
display important details, for instance details
about the location of the football.
This phenomenon is illustrated in the
graph below
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Another important factor for mobile Tv is how the
bitrates allocated to audio and video streams
interact to affect the perceived Qos.
Multimodal quality for generic clips is predicted by a
regression equation of the form AQ+VQ+(AQ*VQ)
where AQ and VQ stand for Audio and Video quality
respectively.
For high motion clips a reduced equation with just
the interaction term (AQ*VQ) gave the best
predictions of multimodal quality
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Now we examine the effects of varying image
resolution and encoding bitrate on service
acceptability.
When the bitrate available for video content is
scarce, reducing the image resolution could
free up valuable encoding bitrate to improve
perceived quality.
Similarly when bitrate is abundant there will
be less loss of detail as the image resolution
is reduced
Image resolution effects depend on the
content
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Careful analysis in the form of a survey
indicates different acceptable ratings for
varied content types
News
In this case the largest image failed to obtain
highest acceptability ratings. In fact
acceptability increased when the resolution
was reduced to 208*156.
Acceptability of News Content
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Number of reasons such as text details, the
headline text, clock, the logo or the captions
for the people being interviewed and factors
such as facial details, shot types etc were the
main reasons for unacceptability by the
viewers of the news channel.
Sports Content
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Live sporting events like the NBC playoff still
need to takeoff with high acceptability ratios.
The main problem in such sporting events is
loss of object detail. The major problem being
locating the ball and identifying the players.
The second most common complaint is about
certain short types – specifically long shots of
the entire pitch to be seen on the small
screen
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Another prominent drawback is inability to
read text detail about the teams and the
scores.
To a large extent these problems can be dealt
by increasing both Image resolution and
Video bitrate.
Acceptability of Sports Content
Animation Content
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A reduction in image size had very little
impact on the acceptability factor.
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Problems such as identifying animal species
when the image resolution was very small
and images being very dark persisted but are
not very prominent.
Why Was Quality Unacceptable
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Across content types, the effect of reducing
image resolution is more pronounced when
bandwidth is abundant.
The best feasible solutions in such scenarios
would be to encode at the largest image
resolution possible for any content type
This also has two exceptions – News and
very low bandwidth Music videos.
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More effective way is to stream text
information separately to the device.
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Protocols such as the SMIL should be
integrated into mobile TV production process
to synchronize text and video streams to
mobile devices.
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Still evaluation is required to understand how
audio and video qualities interact to bias
users perception of video quality acceptance.
Voltage Scaling
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Saving on energy is the most critical factor for
mobile multimedia applications.
A challenging problem is how to minimize
energy consumption while provisioning QoS.
Previously implemented DVS (Dynamic
Voltage Scaling) algorithm reduces the CPU
energy by reducing the CPU speed based on
the application CPU demand.
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But the effectiveness of such an algorithm is
dependent on the prediction of application
demand.
Over prediction may waste energy and under
prediction may degrade the application
performance.
The solution put forth here is PDVS (Practical
voltage scaling) algorithm.
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The PDVS extends traditional real-time scheduling
by deciding what execution speed in addition to
when to execute what applications.
PDVS makes these decisions based on
1.Discrete speed levels of the CPU
2.The total power of the device at different speeds.
3.The probability distribution of CPU demand of
multimedia applications.
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The goal of PDVS is to minimize the total
energy of the mobile device while providing
soft performance guarantees to each
multimedia task.
The purpose is to save energy since CPU
may run slower without affecting application
performance.
Initially we can set a uniform sped for all
concurrent tasks until the task set changes.
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Assume there are n tasks and each task is allocated
Ci cycles per period Pi. The total number of cycles
demanded per second (Hz) by all tasks
The uniform speed can be the lowest speed that is no
less than the total demand i.e
If each task uses its allocated cycles exactly, this will
minimize CPU energy.
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Multimedia tasks often change their cycle demand
dynamically. A task may complete a job earlier even
before utilizing the allocated cycles.
This causes the CPU to be idle resulting in wastage
of power during the idle time.
The PDVS dynamically adapts the CPU speed of
each job execution in a manner that minimizes the
total energy consumed during job execution while
bounding the jobs execution time.
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To attain this a time budget is allocated for each job.
Consider there are n concurrent tasks and each task
is allocated Ci cycles per period Pi then the ith task
is allocated
time units per period Pi.
But another inherent problem is how to dynamically
adapt the execution speed for each individual task.
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To achieve this PDVS sets a speed for each
of the cycles allocated to the job.
If a cycle x,1<x<c is executed at speed
s(x),the execution time is 1/s(x).
Energy consumed by device in this time
interval is
Here ps(x) represents the total power
consumed by the device.
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Expected energy for this task is given by the
equation
Where F(x) is the tail distribution function.
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formulated as
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Although we have arrived at a solution,it is
not quite feasible as number of allocated
cycles C may be very large.
Secondly overhead for optimization may be
unacceptably large.
Hence a piece wise approach is followed.
We use set of points b1,..bk+1 to divide the
allocated cycles into k groups each with a
size gi.
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Thus we get a new constrained optimization
equation as
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PDVS stores speed schedule of a task into its
process control block and updates the speed
schedule when the tasks time budget
changes.
Architecture of PDVS Implementation
Prioritization of Data in MPEG-4
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All the applications on mobile tv require a real
time transmission of video data over fixed
and mobile networks with varying bandwidth
requirements and error rate characteristics.
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These signals must be compressed before
transmission in order to optimize the required
bandwidth to provide a multimedia service.
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As MPEG-4 coded video data is highly
sensitive to information loss and channel bit
errors, the decoded video quality is bound to
suffer at high channel bit error rates.
Coded bit streams are transmitted in form of
packets and the enclosed video payloads are
exposed to channel errors and excessive
delays and thereby information loss.
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The worst effect of bit error occurs when
synchronization is lost and the decoder is no
longer able to identify the received
information belongs to which part of the
frame.
Real time video transmissions are sensitive
to time delays, therefore there is no point of
retransmitting the erroneous video data.
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To overcome these issues one of the
approach is multi-layered video coding.
The compressed bit stream of each video
object plane (VOP) in the video sequence
consists of a number of layers.
There are two layers namely the base layer
and enhancement layers.
The base layer contains essential information
regarding texture reconstruction.
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A similar method employed to improve the quality of
video transport over networks is the prioritization of
different parts of the video bit stream by sending
data as different separate streams.
This enables video encoder to demand the network
to send the data using channels with different
priorities.
Thereby allocating more important and error
sensitive data to more reliable and secure channels.
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MPEG-4 detects entities in the video frame
that the user can access and manipulate
thereby providing user with content based
functionalities for processing and
compression of any video scene
The entities are called video object plane.
The diagram shows Vide object plane
consists of Vop headers and video packets.
a) Video object plane b) Video object
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The Vop and Vp contain synchronization
code and compression parameters.
Each video frame starts with a start code
Start code are unique combination of bits that
never occur in the video data.
The MPEG-4 in addition to coding the texture
and motion information as in traditional block
based video coders,it also codes the shape
of each VOP.
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Errors have detrimental effect on decoded video
quality due to interdependencies of video data.
Synchronization word error is caused by bit rate
variability characteristic, the decoder in such cases
skips all the correct bits waiting to recover the state
of synchronization.
This can be eliminated by placing the coded video
data into regular sized packets, with
resynchronization words between each packet
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MPEG-4 data partitioning succeeds in
ensuring that much of the packet data is not
very sensitive to error.
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An example of a walking person sequence is
used to analyze the error sensitivity of data in
first and second partitions of an MPEG-4
video packets.
a)Error free sequence b)shape data
c)motion data d)Texture Data
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Here we can see that texture errors can be
concealed, the concealment of motion and
shape data results in reconstructed frames
contain a high degree of distortion.
Corruption of texture has negligible effects
Whereas shape data proves to be highly
sensitive
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We now examine as to how prioritization is
effective.
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In this case we consider a GPRS data is
transmitted over packet data traffic channel
after being error protected using one of the
channel protection schemes CS-1,CS-2,CS-3
or CS-4.
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To eliminate these problems video frame is
MPEG-4 encoded and separated into two
segments with high priority and low priority.
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Then the video data are produced by the
encoder and the RTP/UDP/IP protocols are
encapsulated to them.
Two separated segments
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The diagram below shows the objective
quality for single and partitioned stream
transmissions.
Conclusion
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The concept of Mobile Tv is still in its naïve
stages and many issues related to broadcast,
handoff, uninterrupted transmission still
require further study for successful takeoff in
the near future.
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Mobile Television faces the challenge of
being compared with traditional television.
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Though it is very difficult to match the
standards, maximum acceptability can still be
achieved by enhancing performance to the
peak levels and banking on the concept of
being mobile and connected at any place.
References
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[1]. DVB-H: digital broadcast services to handheld devices Faria, G.;
Henriksson, J.A.; Stare, E.; Talmola, P.; Proceedings of the IEEE
Volume 94, Issue 1, Jan. 2006 Page(s):194 - 209
[2]. Systems 2: mobility and video: Can small be beautiful?:
assessing image resolution requirements for mobile TV Hendrik
Knoche, John D. McCarthy, M. Angela Sasse November 2005
proceedings of the 13th annual ACM international conference on
Multimedia MULTIMEDIA '05 Publisher: ACM Press
[3]. Prioritisation of Data Partitioned MPEG-4 for Streaming Video in
GPRS Mobile Networks Jafari,M.;Kasaei,S.; Internet, 2005.The First
IEEE and IFIP International Conference in Central Asia on 26-28
Sept. 2005
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Performance Analysis and multimedia over Wireless:Practical
voltage scaling for mobile multimedia devices:Wanghong Yuan,
Klara Nahrstedt October 2004 Proceedings of the 12th annual ACM
international conference on Multimedia Publisher: ACM Press