TUC Information and Network LabVideo Source Traffic Modeling1 Χαρακτηρισμός και...

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


Presentation Flow

Uncompressed Video Characteristics Compression Techniques Compressed Video Semantics Burstiness Statistical Multiplexing Need for Video Source Modeling Basic Modeling Methods General classification Conclusions


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


Video Semantics(2)


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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)


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…


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

TUC Information and Network LabVideo Source Traffic Modeling1 Χαρακτηρισμός και...

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