TUC Information and Network LabVideo Source Traffic Modeling1 Χαρακτηρισμός και...
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TUC Information and Network Lab Video Source Traffic Modeling 1
Χαρακτηρισμός και Μοντελοποίηση Πηγών Video
Characterization and Modeling of Video Sources
Γαβαλάκης Πέτρος
Πέμπτη, 22 Μαρτίου 2001
TUC Information and Network Lab Video Source Traffic Modeling 2
Presentation Flow
Uncompressed Video Characteristics Compression Techniques Compressed Video Semantics Burstiness Statistical Multiplexing Need for Video Source Modeling Basic Modeling Methods General classification Conclusions
TUC Information and Network Lab Video Source Traffic Modeling 3
Video Semantics
A typical video sequence is usually described as a collection of independent video shots called scenes.
Each scene is an ordered set of video frames depicting a real-time continuous action
Except from static real-world scenes, a typical video sequence consists of various artificial effects (e.g. camera movement , zooming,picture in picture,graphics etc.)
The assumption of scene independence does not seem to hold in most video sequences
The scenes are in fact correlated.There is a low probability of having a short scene while the probability of having long ones decades relatively slow
On the contrary to the scene complexity, the scene length seems to be not correlated
TUC Information and Network Lab Video Source Traffic Modeling 5
Uncompressed Video Streams
The nature of video information sources varies widely depending on: Content of video per scene Scene details and object or camera movement Quality required Various video streams (e.g. videophone,movie,news,etc.)
have different statistics (e.g. scene length duration) Bandwidth requirements for uncompressed video 100-240 Mbps Uncompressed HDTV streams require around 1Gbps for
proper delivery Redundancy – need for compression
TUC Information and Network Lab Video Source Traffic Modeling 6
Video Compression Techniques
Compression is achieved by removing redundancy Redundancy types in video signal:
• within a video frame (intra-frame): spatial• between frames in close proximity (inter-frame) : temporal
Compression methods:• Lossless or lossy
• Exact recovery or not
• Symmetric or asymmetric• Same amount of computational effort in coder and decoder or not
MPEG-1 • Quality similar to that of a VHS• Bit-rate approximately 1.2 Mbps
MPEG-2• Broadcast quality video• Bit-rate: 4-6 Mbps• Wide range for rate and resolution
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Characteristics of compressed video
Statistical behavior is described by using histograms and correlation functions
Correlations arise as a consequence of visual similarities between consecutive images (or parts of images) in video streams
Compression results in a reduction in these correlations Compressed stream still contains considerable amount of
correlations Correlation along with different information content of
each frame leads to burstiness
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Classification of compressed video variability
Interframe variability: Discontinuous variation before and after change of scene Long term
Intrascene variability: Short term Smooth variation with temporal correlations Occasional large variations due to subject and camera motion
Intraframe variability: Variations that have periodicity due to image scanningand block processing by encoding techniques Usually smoothed by the use of pre-buffers during encoding
and packetization
TUC Information and Network Lab Video Source Traffic Modeling 9
Burstiness
One simple definition: Ratio between peak and average bit rate (PAR)
Burstiness indicates the presence of non-negligible positive correlations between cell inter-arrival times
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Other Bit-Rate Burstiness Measures
Bit-rate distribution Average bit-rate Peak bit-rate Variance
Autocorrelation Function Expresses temporal variations
Coefficient of Variation Uncorrelated streams have fixed CoV Useful when measuring multiplexing characteristics when VBR
signals are buffered and statistically multiplexed Scene duration distribution Average duration of peaks
Useful to estimate probability of buffer overflow
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Burstiness – Network issues
“If much of the traffic is bursty it results in poor network performance”
The above is true when deterministic multiplexing is used by the network: Each connection is allocated its peak bandwidth Large amounts of bandwidth is wasted for bursty connections
If burstiness is adequately reflected in network management it can lead to multiplexing gain
Need for statistical multiplexing
TUC Information and Network Lab Video Source Traffic Modeling 12
Statistical Multiplexing 1/2
The statistical multiplexer can be seen as a finite capacity queueing system with buffer size B (in cells) and one server with service rate C
TUC Information and Network Lab Video Source Traffic Modeling 13
Statistical Multiplexing Model 2/2
In statistical multiplexing, a VBR connection is allocated its “statistical” bandwidth (less than its peak but more than its average bit rate)
The statistical bandwidth of a connection depends not only on its own stochastic behavior but also strongly on the characteristics of existing connections
Bandwidth gain is attained by allowing the sum of the peak rates of the input streams exceed the service rate of the multiplexer
This results in queuing and possible buffer overflow Need for estimates for a number of independent multiplexed signals:
Total statistical transmission bandwidth Resulting utilization Required buffer size Probability of buffer overflow Rate of packet discard Distribution of packet delay
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Why model video signals
To help user define requirements for network QoS -> contract
Aid for designing future communication networks using statistical multiplexing (ATM) – Performance evaluation Predict:
• Delay arising from statistical multiplexing• Buffer size required for statistical multiplexing• Bandwidth required for multiplexed streams
Results used in conjuction with traffic analyses of voice and data signals to design averall bandwidth.
Admission and policing algorithms to effectively allocate dynamic resources(buffers,bandwidth) to streams
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Effective bandwidth and buffer
One purpose of modeling is determining the “effective” bandwidth and buffer size
Definition: the minimum amounts of bandwidth and buffer needed to guarantee a certain level of QoS for the multiplexed streams
Ways of calculation/prediction Simulations based on actual data Problem: In regions of extremely low probability (fore
example estimation of maximum delay) Mathematical analysis
• Construction of approximate mathematical models• Models are used to help analysis in low probability regions• Numerically or by explicit expressions
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Traffic models used so far…
Data signals: Assumptions
• Random packet arrival • No correlations between arrivals
Speech signals: Model speech an silence interval durations On-Off model (mini source) During “on” fixed inter-arrival time
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Modeling difficulties 1/2
For modeling purposes a network can be viewed as a collection of queues
A server corresponds to a transmission link and there is a finite buffer associated with each server
Even if incoming traffic is characterized satisfactorily it’s impossible to solve these queuing problems numerically in real-time
A well-known approach is to decompose the network into individual queues and analyze each one in isolation
Even with this assumption that the arrival process of a connection has the same stochastic with the source,the solution is still unfeasible if the number of the multiplexed arrival streams is very large
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Modeling Difficulties 2/2
Difficulty to apply in a multi-hop scenario Useful to Offline dimensioning problems via simulations Even if feasible, the queuing analysis is done over an
infinite time and for very large buffers which is unrealistic The use of video models in online traffic control is still an
open research issue
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Different approaches for video source modeling
Use detailed stochastic source models Matching various statistics of video source Content-based approach Different approach for Real-time video sources
Use stochastic bounds to characterize a source
Characterize each video source by a deterministic time-invariant traffic envelope
TUC Information and Network Lab Video Source Traffic Modeling 20
Noticed features of coded video
The distribution of bit-rate variations is bell-shaped The auto-correlation is close to a decaying exponential
function Coefficient of variation increases duo to positive
correlation of the bit-rate distribution When multiplexing a series with no temporal correlations
less data accumulates in the buffer than would with a real video signal
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Basic Modeling Methods Overview (1/2)
AR Model• Zero-order -> random series• Simulation method• Doesn’t model scene changes• Tries to match statistics with parameters of model
Maglaris Markov model• FSM:Each state corresponds to a possible packet rate level• intra-scene bit-rate variations are smooth ->only transitions to
adjacent states are generated ->birth/date Markov model• Analytic form for determining SM effects for N sources• Doesn’t model scene changes:
Simple Scene Change Model
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Basic Modeling Methods Overview (2/2)
Sen Markov Model • Markov Modulated Poisson Process model • Two levels {high,low} for bit-rate• Analytic• Models scene changes
Yamada Markov Model• Analytic• Two-dimensional• Attempts to model rapid increase in bit-rate at a scene change
TUC Information and Network Lab Video Source Traffic Modeling 23
Equivalent Bandwidth method
Each source is of “on-off” type Parameters of the model
• Peak traffic rate during “on”• Average length of “on”• Average length of “off”• Source activity
Closed form of the source’s equivalent bandwidth Realistic when using leaky-bucket The result is often highly conservative The useful admission region for many types of sources can
be significantly larger than this estimate
TUC Information and Network Lab Video Source Traffic Modeling 24
Gaussian Approximation
Each connection is characterized by the average and the standard deviation of its bit-rate
The method is based on the simplifying assumption that if the number of sources multiplexed is large,total traffic behaves as a Gaussian process with mean and variance the sum of the means and variances of the sources, respectively
Closed form for two useful estimates Overflow probability Upper bound for cell loss probability
It treats all connections as if they have the same cell loss requirements
It doesn’t take the buffer size into consideration
TUC Information and Network Lab Video Source Traffic Modeling 25
Compromise approach
A traffic stream with peak rate R,mean rate λ and equivalent bandwidth C feeding into a buffer of size B is equivalent to an on-off stream with peak rate C and mean rate λ, feeding into a buffer of size zero
Results show that this approach can be overoptimistic when buffer size is small and may lead to higher cell loss ratios than the user’s desired QoS requirements
TUC Information and Network Lab Video Source Traffic Modeling 26
Content-based approach
This approach is not based only on matching the various statistics of the original source but rather it includes characterization of video content
Need for models to capture both real video styles and various compression techniques used
Since the encoded video stream is a combination of scene characteristic and coding algorithm specific mapping,it is desirable to separate them by identifying the independent descriptor variable characterizing the scene,frame anf objects.
Such descriptors may be the scene length,the scene complexity and the scene motion…
To model intrascene objects,each scene can be further divided in subscenes and modeled using the same model
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General Classification of Video Source models
Short-range models- Burstiness is significantly decreased when using SM Renewal models
• No correlations Markovian and auto-regressive models (AR)
• Exponentially decaying autocorrelations / inter-arrival times Sub-exponential models
• The decay of the autocorrelations is slower than exponential but faster than a power function
Long-range dependent (LRD) models –SM can even increase burstiness Autocorrelations decay as a power function Slower decay -> significant correlations even for large lags
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Modeling Conclusions (1/2)
Utilization:sum of the average bit-rates to channel capacity Definition
Results from simulation indicate that there is an improvement in cell loss performance as more video streams are multiplexed when using both the actual data and the corresponding model.(close performance)
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Modeling Conclusions (2/2)
Parameters are difficult to estimate in real-time Usually a training sequence of video streams is used to
estimate parameters for a category (based on content) of videos
Effectiveness of statistical multiplexing: The average delay is smaller for larger number of multiplexed
signals Estimating the appropriate traffic descriptors for VBR video
is a challenging research issue that awaits further work…
TUC Information and Network Lab Video Source Traffic Modeling 30
References
Admission Control and Bandwidth Allocation in High-Speed Networks as a System Theory Control Problem by Ephremides,Wieselthier,Barnhart
Modeling video sources for real-time scheduling by Lazar,Pacifici,Pendarakis
Traffic Models in Broadband Telecommunication Networks by Abdelnaser Adas
Statistical Multiplexing by Marwan Krunz A traffic model for MPEG-coded VBR streams by Marwan
Krunz,Herman Hughes A content-based approach to VBR Video Source Modeling
by Bocheck,Chang