1. TUC Information and Network LabVideo Source Traffic Modeling1 Χαρακτηρισμός και Μοντελοποίηση Πηγών Video Characterization and Modeling of Video Sources Γαβαλάκης Πέτρος Πέμπτη, 22 Μαρτίου 2001

2. TUC Information and Network LabVideo Source Traffic Modeling2 Presentation Flow Uncompressed Video Characteristics Compression Techniques Compressed Video Semantics Burstiness Statistical Multiplexing Need for Video Source Modeling Basic Modeling Methods General classification Conclusions

3. TUC Information and Network LabVideo Source Traffic Modeling3 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

4. TUC Information and Network LabVideo Source Traffic Modeling4 Video Semantics(2)

5. TUC Information and Network LabVideo Source Traffic Modeling5 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

6. TUC Information and Network LabVideo Source Traffic Modeling6 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

7. TUC Information and Network LabVideo Source Traffic Modeling7 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

8. TUC Information and Network LabVideo Source Traffic Modeling8 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 scanning and block processing by encoding techniques Usually smoothed by the use of pre-buffers during encoding and packetization

9. TUC Information and Network LabVideo Source Traffic Modeling9 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

10. TUC Information and Network LabVideo Source Traffic Modeling10 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

11. TUC Information and Network LabVideo Source Traffic Modeling11 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

12. TUC Information and Network LabVideo Source Traffic Modeling12 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

13. TUC Information and Network LabVideo Source Traffic Modeling13 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

14. TUC Information and Network LabVideo Source Traffic Modeling14 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

15. TUC Information and Network LabVideo Source Traffic Modeling15 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

16. TUC Information and Network LabVideo Source Traffic Modeling16 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

17. TUC Information and Network LabVideo Source Traffic Modeling17 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

18. TUC Information and Network LabVideo Source Traffic Modeling18 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

19. TUC Information and Network LabVideo Source Traffic Modeling19 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

20. TUC Information and Network LabVideo Source Traffic Modeling20 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

21. TUC Information and Network LabVideo Source Traffic Modeling21 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

22. TUC Information and Network LabVideo Source Traffic Modeling22 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

23. TUC Information and Network LabVideo Source Traffic Modeling23 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

24. TUC Information and Network LabVideo Source Traffic Modeling24 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

25. TUC Information and Network LabVideo Source Traffic Modeling25 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

26. TUC Information and Network LabVideo Source Traffic Modeling26 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

27. TUC Information and Network LabVideo Source Traffic Modeling27 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

28. TUC Information and Network LabVideo Source Traffic Modeling28 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)

29. TUC Information and Network LabVideo Source Traffic Modeling29 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…

30. TUC Information and Network LabVideo Source Traffic Modeling30 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