1. Researches in Telecommunications at Izhevsk State Technical University Albert Abilov Seminar at Chair of Telecommunications, TU Dresden October 21, 2008

2. What would i like to tell today about Grant for my staying at TU Dresden Where do i live and work Several words about me The main researches made in past Tools for telecom courses 2

3. Grant for my staying at TU Dresden Scholarship of «Mikhail Lomonosov»-Programme: Research Grants and Research Stays for Doctoral Candidates and Young University Teachers from the Natural Sciences and Engineering Scholarship is jointly granted by DAAD (www.daad.de) and Russian Education Ministry (www.ed.gov.ru) Host part is Chair of Telecommunication, TU Dresden (www.ifn.et.tu-dresden.de/tk), Prof. Dr.-Ing. Ralf Rehnert The period of stay for research is 3 months 3

4. Where do i live and work My District and City Udmurt Republic is one of 85 districts of Russia Izhevsk is Capitol of Udmurt Republic Population of Izhevsk is about 650 000 people Izhevsk is located about 1 100 km from Moscow Udmurt Republic: www.udmurt.ru Izhevsk: www.izh.ru 4

5. Where do i live and work My University Izhevsk State Technical University is one of 4 State universities in Izhevsk There are about 10 000 students and 14 faculties in the most of technical areas. Izhevsk State Technical University: University has cooperation and student/researcher exchanges with many Russians and abroad universities. www.inter.istu.ru It was created in 1952 5

6. Where do i live and work Our Chair Faculty of Instrumentation Engineering Radio Engineering Equipments and methods of quality control Design of radio-equipment Electrical Engineering Laser systems Physics Telecommunication networks and systems http://www.istu.ru/unit/prib/netChair of Telecommunication Networks and Systems: Specialities for students –Telecom networks and switching systems –Transmit telecom systems Labs –Switching systems –Electronics lab –Communication networks Department (Chair) was created at 1998 6

7. Several words about me ALBERT ABILOV Candidate of Science, Docent in Izhevsk State Technical University Address: 7, Studencheskaya str. Izhevsk, 426069, RUSSIA Office: Izhevsk State Technical University Building 1, Floor 4, Room 403 Phone/fax: +7 3412 580399 Mobile: +7 9128 562202 E-mail: abilov@udm.ru My contacts WWW: http://www.istu.ru/unit/prib/net/abilov 7

8. Candidate of Science (PhD) theses Creation of mathematical models of mobile communication systems Research and design algorithms for optimal receiving of digital signals Creation of realistic algorithms for receiving of digital signals and for control of forward channel state in mobile system Creation of simulation model for control algorithms Analysis of efficiency of former and offered algorithms for receiving of digital signals and for control of forward channel state by means of simulation Design of hard- and software facilities for realization of offered algorithms in subscriber station of “Volemot” mobile system Trial (field) testing and experimental evaluation of offered algorithms efficiency Design and research of digital signal estimation and optimal utilization of frequency resource algorithms in mobile telecommunication system Supervisor: Prof. Vladimir V. Khvorenkov The main tasks: 8

9. Candidate of Science theses Math model of digital mobile communication channel Design and research of algorithms of digital signal estimation and optimal utilization of frequency resource in mobile telecommunication system – state vector; – estimation vector; – errors vector; – control vector; Supervisor: Prof. Vladimir V. Khvorenkov Source of control Source of information codewords Source of errors For estimation D – delay; 9

10. Candidate of Science theses Design and research of algorithms of digital signal estimation and optimal utilization of frequency resource in mobile telecommunication system Criterion of channel quality is minimum of bit errors ratio (BER) Supervisor: Prof. Vladimir V. Khvorenkov Sources of errors Source of information codewords Errors estimation Quality of channels estimation Model of control channel searching in mobile system 10

11. Candidate of Science theses Algorithm of digital information receiving in signaling channels of “VOLEMOT” mobile system. Results of simulation Design and research of algorithms of digital signal estimation and optimal utilization of frequency resource in mobile telecommunication system Codeword structure Algorithm which was: compare of two nearby codewords during fix time Offered and realized algorithm: voting method Supervisor: Prof. Vladimir V. Khvorenkov Bit error probability Probability of codeword receive Correct receive for former algorithm Correct receive for offered algorithm False receive for offered algorithm False receive for former algorithm Correct receive for offered algorithm with reduced probability of false receive 11

12. Candidate of Science theses Algorithm of digital information receiving in signaling channels of “VOLEMOT” mobile system. Results of simulation Design and research of algorithms of digital signal estimation and optimal utilization of frequency resource in mobile telecommunication system Codeword structure Offered synchronization byte: 01111110 Supervisor: Prof. Vladimir V. Khvorenkov Probability of codeword receive Bit error probability Correct receive for offered algorithm with new synchro-byte Codeword structure Offered Former Correct receive for offered algorithm with former synchro-byte Correct receive for former algorithm with former synchro-byte 12

13. Candidate of Science theses Design and research of algorithms of digital signal estimation and optimal utilization of frequency resource in mobile telecommunication system Supervisor: Prof. Vladimir V. Khvorenkov Simulation model of control channel searching in mobile system 13

14. Candidate of Science theses Simulation model of control channel searching in mobile system Design and research of algorithms of digital signal estimation and optimal utilization of frequency resource in mobile telecommunication system = 0,001587 = 0,002832 Former control algorithm: Offered control algorithm: Criterion of efficiency: average bit errors ratio on the simulation interval = 0,01 Threshold for changing channel: Supervisor: Prof. Vladimir V. Khvorenkov 14

15. Candidate of Science theses Realization and operational testing (trial) of algorithms –The developed algorithms were realized in Mobile subscriber terminal URAL-RS6 for mobile system VOLEMOT (Russia) –Bit error rate measurement on the real mobile network (VOLEMOT) Design and research of algorithms of digital signal estimation and optimal utilization of frequency resource in mobile telecommunication system Supervisor: Prof. Vladimir V. Khvorenkov 15

16. Candidate of Science theses Realization and operational testing (trial) of algorithms on real system Design and research of algorithms of digital signal estimation and optimal utilization of frequency resource in mobile telecommunication system = 0,002538 = 0,004809 Offered control algorithm Supervisor: Prof. Vladimir V. Khvorenkov Former control algorithm How threshold for changing channel influence on average BER and gain (results of simulation and experiment) Gain: Average BER for former algorithm Average BER ratio for offered algorithm 16

17. Applications for network planning Tool for cellular radio subsystem planning Realization of model in network planning tool Features of tool: approximate coverage of cell calculation; network configuration planning Interface Parameters of network Factors of Hata model Switching center parameters Base station parameters Co-author: Roman Semieshin 17

18. Applications for network planning Tool for urban and rural telephone networks planning Realization of famous models in network planning tool Co-author: Alexey Susekov Features of tool: traffic calculation; trunk lines calculation; for urban and rural applications; network planning and traffic forecasting. It is now utilized for: educational process Interface Switching station parameters Types of traffic 18

19. Telecom infrastructure development Research Project № П-1-02: Conception of telecommunication infrastructure development in Udmurt Republic till 2010 year Grant: Ministry of fuel, energy and communication of Udmurt Republic, Russia Advisor and Principal Investigator: Albert Abilov To analyze dynamic and state of the art of info- communication development in World, Russia and Udmurt Republic To determine the most important trends, basic views and regulations concerning telecommunication networks and services development in the Udmurt Republic up to the year 2010 Basic objectives and tasks of the conception: Expected resulting effect: Realization of the conception will reduce the lag of the Udmurt Republic in the world basic telecommunication indices and will facilitate to provide people and organizations with high-quality communication services Conception (220 pp.) has been approved and accepted for realization by Government of Udmurt Republic (Russia) in June 2004 19

20. Impact economics & education on ICT World trends of info-communications development –General analysis of info-communications development Research Project № 07-07-07009: Grant: Russian Foundation for Basic Research, Russia (http://www.rffi.ru/eng/) Advisor and Principal Investigator: Albert Abilov Percentages of Internet users over the world (2007 year) Key ICT indicators in dynamic а) Developed economiesb) Developing economiesc) Poor economies 20

21. Impact economics & education on ICT World trends of info-communications development –Wired telephone communication dynamics Research Project № 07-07-07009: Grant: Russian Foundation for Basic Research, Russia (http://www.rffi.ru/eng/) Advisor and Principal Investigator: Albert Abilov а) Developed economies b) Developing economies c) Poor economies 21

22. Impact economics & education on ICT World trends of info-communications development –Mobile cellular communication dynamics Research Project № 07-07-07009: Grant: Russian Foundation for Basic Research, Russia (http://www.rffi.ru/eng/) Advisor and Principal Investigator: Albert Abilov а) Developed economies b) Developing economies c) Poor economies 22

23. Impact economics & education on ICT World trends of info-communications development –Internet dynamics Research Project № 07-07-07009: Grant: Russian Foundation for Basic Research, Russia (http://www.rffi.ru/eng/) Advisor and Principal Investigator: Albert Abilov c) Poor economies b) Developing economies а) Developed economies 23

24. Impact economics & education on ICT What main factors can impact on ICT development? –Economics (GDP per capita – Gross Domestic Product per capita) Research Project № 07-07-07009: Grant: Russian Foundation for Basic Research, Russia (http://www.rffi.ru/eng/) Advisor and Principal Investigator: Albert Abilov Average info-communication indicators at the year-end of 2007 Development indicatorsDeveloped coun tries Developing countri es The poorest countries Telephone lines density, %48,124,41,7 Mobile cellular density, %109,599,625,9 Internet users density, %59,537,93,8 Broadband subscribers density, %22,47,40,05 *GDP per capita, thousand $49,624,51,7 * At the year-end of 2006 –Education (EI – Educational Index) its method of calculation is defined in UN Development Programme (UNDP) Education Index values averaged by country groups Indicator Developed countr ies Developing countri es The poorest countrie s Adult literacy, % (among people at the age of 15 and older)97,995,955,9 Combined primary, secondary and tertiary school enrollment level, %91,782,453,8 Education Index0,960,910,55 were k – sequence number of country; n – number of countries under examination; Ri, Rj – country ranks according to respective indicators. The Spearmen ranking method enables to estimate, how close the parameters interrelation is. 24

25. Impact economics & education on ICT ICT and Economics Research Project № 07-07-07009: Grant: Russian Foundation for Basic Research, Russia (http://www.rffi.ru/eng/) Advisor and Principal Investigator: Albert Abilov 25

26. Impact economics & education on ICT ICT and Economics Research Project № 07-07-07009: Grant: Russian Foundation for Basic Research, Russia (http://www.rffi.ru/eng/) Advisor and Principal Investigator: Albert Abilov Indicators of mutual influence of info-communication (2007) and economics (2006) Indices of mutual influence Telephone lines densityMobile cellular densityInternet users densityBroadband subscr. density Equation of correlation line y 0,0091x 0,8439 0,6109x 0,5223 0,0184x 0,7856 8E-5x 1,3625 Spearmen Index ρ 0,8880,8610,8500,864 Interrelation between Telephone lines Density and GDP per capitaInterrelation between Mobile Cellular Density and GDP per capita Interrelation between Internet Users Density and GDP per capita Dynamics of Spearmen’s Index 26

27. Impact economics & education on ICT ICT and Educational level Research Project № 07-07-07009: Grant: Russian Foundation for Basic Research, Russia (http://www.rffi.ru/eng/) Advisor and Principal Investigator: Albert Abilov 27

28. Impact economics & education on ICT ICT and Educational level Indicators of interrelationTelephone lines densityMobile subscr. densityInternet users densityBroadband subscr. density Equation of correlation line y0,0212e 7,6275x 1,7416e 4,0555x 0,0565e 6,6709x 5E-5e 11,924x Spearmen Index ρ0,8540,7210,7940,789 Research Project № 07-07-07009: Grant: Russian Foundation for Basic Research, Russia (http://www.rffi.ru/eng/) Advisor and Principal Investigator: Albert Abilov Indicators of mutual influence of info-communication (2007) and Educational Index (2006) Dynamics of Spearmen’s Index Interrelation between Telephone lines Density and EI Interrelation between Mobile Cellular Density and EI Interrelation between Internet Users Density and EI 28

29. Educational tool for telecom courses Signalization in telecommunication networks The main goal is to give the best understanding of signalization principles by means texts, pictures and animations Co-author: Vladimir Prozorov Several examples: Channel associated signalization 29

30. Educational tool for telecom courses Signalization in telecommunication networks The main goal is to give the best understanding of signalization principles by means texts, pictures and animations Co-author: Vladimir Prozorov Several examples: Common channel signalization №7 30

31. Models and algorithms for live streaming networks with feedback Albert Abilov Seminar at Chair of Telecommunications, TU Dresden October 21, 2008

32. What would i like to tell today about Multimedia Streaming Conception Problems and approaches for P2P Streaming Robustness in P2P Streaming Networks Mathematical models for the Streaming System Estimation and Feedback control algorithms Simulation for simplest case Some questions for the research 2 This research has been supported be Swedish Institute and DAAD

33. Multimedia streaming conceptions Client/Server Architecture –Routers can use IP Multicast or IP unicast protocols –Clients (PCs) are directly connected to Server –Difficult realization new protocols on the network –Limited deployment on the Internet, content-distribution-networks technologies are costly yet –IP multicast requires support at all routers Peer-to-Peer Overlay Architecture –Last several years multicast services are more and more considered at the application level –Overlay approach to Multicast is used –Clients act as both customer and intermediate nodes –Peers convey the live streaming content –IP Unicast on the IP level is used –P2P conception is used for Network Architectures –Low cost for deployment Main approaches for live streaming IP level Application level Client Server Router IP level Application level Peer Server Router 3

34. Problems and approaches for P2P streaming Large population of users requires high transmission capacity at the streaming server P2P approach aims to alleviate these demands –Peer uses the upload bandwidth for distributing media stream The number of peers in the overlay may change rapidly Streams are transmitted with end-to-end delays There may be interrupts of connection caused by the frequent joining and leaving of individual peers The network must be as more as flexible the must be self-adapting and have possibility to change its parameters (network structure, FEC redundancy, etc) dynamically in depends on changing conditions Main problems for P2P streaming Main approaches are considered today by research community Push Method –Single-Tree-Based Overlays  Routing based Overlay  Peer-Based Overlay –Multiple-Tree-Based Overlays Pull Method –Mesh-Based Overlays …are not considered as perspective 4

35. Problems and approaches for P2P streaming Routing-Based Overlay –Reproduce the native IP Multicast structure –Servers are mounted with programmable routing functions –Servers use upstream capacity for conveying stream data –All servers are stable and do not leave network –High reliability, low flexibility and high cost Peer-Based Overlay –Peers use upstream capacity for conveying stream data so as to reduce the server load –Each segment (packet) reaches the peer only through one path in the tree –Frequent disconnections of peers can significant degrade the service quality –The most famous projects: SpreadIt, PeerCast, ESM, NICE, D3amcasT and others –The tree structure is fully controlled by Server Push Method: Single-Tree-Based Overlay Routing-Based Overlay for single-tree structure Application level Server Leaves Disjoin Join Peer-based Overlay for Single-Tree Streaming Application level Server Join/Disjoin Programmable Router 5

36. Problems and approaches for P2P streaming Single-Tree Overlay –All segments (packets) go through the same paths –When the peer (parent) leaves the tree:  Server reconstructs the tree structure  All its descendants experience loss packets until the tree is repaired –Buffered data of new parent can preserve segments for children Push Method: Multiple-Tree vs Single-Tree-Based Overlay Multiple-Tree Overlay –The segments are allocated in a round robin manner (in block) to as many as there are trees –Different segments reach the peer through independent overlay paths –If one peer leaves the tree then only one segment is lost in the block –Network or FEC redundancy can recover lost segments –Redundancy requires addition capacity –The most famous projects: SplitStream, CoopNet, P2PCast and other 6

37. Problems and approaches for P2P streaming Download Bandwidth (DB) of the Peer –If the peer has DB and UB larger than the required bandwidth (streaming bandwidth – SB) then it can be part of network –The peer can convey at least one stream –If UB/SB ≥ N and DB/SB ≥ N then peer have possibility to relay N different streams Upload Bandwidth (UB) Allocation Policies –UB = SB  UB of peer is evenly divided among the trees  Each peer relays the stream only to one child in each tree  Min.breadth-max.depth concept –UB ≥ N*SB  Peer relays data in one tree only, but to several (N) child peers  Min.depth-max.breadth concept  More difficulty to maintain the trees in a dynamic scenario Push Method: Download (DB) and Upload (UB) Bandwidth of the Peers … DB UB UB/SB ≥ N SB DB UB … SB UB/SB = N SB – Stream bandwidth DB – Download Bandwidth UB – Upload Bandwidth 7

38. Problems and approaches for P2P streaming Segments pulling concept –Host interested to content requires server a list of peers which are currently received the same content –Host established a partner relationship with subset of peers –Each host receives a buffer maps from its partners –Each peer cashes and shares segments of stream by request –If the peer cannot receive the segment from one peer it requires (pulls) it from other peer –The most famous projects: CoolStreaming, PPLive and other Pull Method: Mesh-Based Overlay Segment 2 1 2 34 5 Segment 1 Advantages –Dynamic overlay which follows the changes of network conditions –Better Resilience Deficiencies –Additional delay at each peer due to requests (pulling) data –Frequent exchange of control messages –Random, hardly predictable performance –Non static network structure 8

39. Robustness in P2P streaming networks The main reasons of segment losses in P2P streaming networks –Physical, Data link and Network and Transport Layers  Delays, congestion, etc  Physical and Data link and Transport Layers can have mechanisms for data recovering (FEC, ARQ) –Application layer  Node churns (joins and leaving network)  All descendants of leaving peer can not receive segments until the tree is repaired Robustness in conditions of node churns … … … Disjoin No stream during searching a new peer Search a new peer The main methods for recovering the lost data –Physical and Data link and Transport Layers can have mechanisms for data recovering  FEC  ARQ –Application Layer can employ:  Multiple Description Coding (MDC)  Forward Error Correction (FEC)  Multiple-tree Approach  Network Redundancy, etc 9

40. Robustness in P2P streaming networks FEC Particularity for P2P Streaming –FEC is not relevant for single-tree-based approach –Packet-level FEC is used –The stream is divided to blocks –Each block has information and redundancy segments Advantages of FEC for P2P Streaming –The limited lost segments in the block can be reconstructed –There is no delay Deficiencies of FEC for P2P Streaming –FEC requires additional resource capacity (bandwidth) Approaches of FEC employment for P2P Streaming –Static FEC (the number of FEC Redundancy Segments is not changed) –Adaptive FEC (the number of FEC Redundancy Segments is regulated in depends on state of the network) –Reed-Solomon code can be used Forward Error Correction (FEC) for P2P Streaming Redundant Segments Data Segments 10

41. Multiple-Tree Structure –Peer nodes are organized in X trees by centralized managements protocol –Root (the Server) plays a central role in construction trees –Each node has one child only –S – the number of root’s children –N – the number of peers –I = N/S – the number of layers in the tree –Root sends only one of packets to in a block to its child in given tree Multiple-Tree-Based Case for UB = SB FEC Redundancy –X = D + R packets are sent per one block  where D – data; R – redundancy –If at least D packets has been correctly received then the block cam be reconstructed –Required Redundancy Level must be determined by packet loss rate in the network –Peers should report to source about the loss rate they experience –The effective feedback control system must be used Multiple Tree Structure 11 Robustness in P2P streaming networks

42. Measurement of loss packet rate –The packet Loss Rate must be measured in the nodes for each tree separately –It is necessary to provide a sufficient accuracy of Packet Loss Estimation 1. Direct Feedback Updates –Each peer measures Packet Loss Rate and sends updates directly to the Root –Measurement is made periodically –Root receives N*X updates and can be overloaded P2P Streaming Structure with feedback (three approaches) Feedback methods for the P2P streaming 2. Feedback Updates from Leafes (from top to down) –Each children-peer measure stream from its parent-peer, aggregates the results and sent update to its descendant –Only Leaves send the feedback updates directly to he root –The root receive only S*X updates 3. Feedback Updates from Root’s children (from down to top) –Updates are sent from child-peers to parent-peers –Root’s children periodically report the root about measured packet loss rate 12 Robustness in P2P streaming networks

43. Measurement of packet loss rate –The root experiences the far less load if it receives updates only from leafs or its children –Accuracy of packet loss tare estimation depends on the sample of measured packets –If the period of updates is one block ( X packets) then estimation accuracy is 1/ X only – The more blocks is used for measurement, the better accuracy of packet loss estimation –If the period of updates is M block ( X packets) then estimation accuracy is 1/ MX Main approaches for the control system (two approaches) 1.On-off control system – Based on step by step increments or decrements of controller output 2.Proportional control system – Number of redundant packets depends on the difference between the calculated and desired loss packet rate Packet Loss Rate Measurement and Control System 13 Robustness in P2P streaming networks

44. Mathematical Models for Streaming System Streaming structure –Data stream is the sequence blocks ( X packets in each block) –The packet is elementary entity in our studies –The packet arrives to the peer through links with different delays or it is lost –t k = X/v – interval between moments k; where v – packet rate Models of direct data streaming channel 14

45. Channel description on the base of the states equation approach – – Data Vector which defined on the Galois Field of the second order GF(2) and describes one block of packets – – Error Vector which describes the loss packet process – Estimation Vector is result of summation and by rule of module 2 where – transition matrix of data source; – transition matrix of error source; – group operation of summation by module 2; k = 0, 1, … – vector estimation phase The format of Data Vector is represented as The Estimation Vector can be presented as –Example:, where the second packet is lost Model of direct data streaming channel without FEC and feedback Mathematical Models for Streaming System Description of the Data Stream Source Description of the Direct Channel 15

46. Models of the Direct Channel and Data Streaming Source Model of direct data streaming channel without FEC and feedback Mathematical Models for Streaming System –The model describes the streaming process in dynamics Example of the Data Streaming Source Model: Model of the channel 16

47. The Streaming Source Model Model of direct channel with fixed FEC-redundancy and without feedback Mathematical Models for Streaming System –The FEC-Redundancy in the Block does not depend on data streaming content but must depend on the feedback information –The streaming source with redundancy can be presented as two separate source:  Data source without redundancy  Redundancy source –Denote the Vectors: – the Data Vector; – Redundancy Vector; –These vectors have the same dimensionality X –The format of Data Vector is represented as: –The format Redundancy Vector is represented as: –In case of fixed redundancy the Vector has one resolved combination only –“1” in the position of denotes a presence of redundant packet in the block 17

48. The Streaming Source Model –Equation of the streaming source with taking into account the redundancy: where – transition matrix of redundancy source –The format of Streaming Vector is represented as: Model of direct channel with fixed FEC-redundancy and without feedback Mathematical Models for Streaming System Model of the streaming source –The example of the streaming vector presentation: –“1” denotes a presence of the data packet; “0” denote a presence of redundancy packet –Streaming Vector has only one resolved combination in case of fixed redundancy This model does not describe the control algorithm generation of the redundancy vector 18

49. Measurements timing –In general the redundancy can be controlled with t k period, i.e. interval of one block –But the number of segments is not enough for required accuracy –The peer must receive as more as possible packets for the good loss rate measurement ( M blocks) –m – the phase of estimation –t m = t k M – period of measurement Packet loss rate measurements Mathematical Models for Streaming System Feedback timing (two approaches) 1.Feedback packets are sent periodically – The period of feedbacks sending is t m F, where F is a number of measurements – If F = 1 then feedback is sent on the each measurement – The feedback period t f value is a research question – The more feedback period, the more accuracy of packet loss estimation but the slower reaction of the control system 2.Feedback packets are sent upon request of node – Threshold criterion – If the estimation of the packet loss rate in the peer is less or more than some threshold then it sends appropriate feedback Feedback timing structure 19

50. Mathematical Models for Streaming System Control timing –Redundancy is controlled by root –One peer only can not be the reason for changing redundancy –The peers send the feedback packets to the root independently and asynchronously –Feedback packets can experience the different delays –The control period is not synchronous with feedback period –The root makes decision every control interval  Decrease redundancy  Increase redundancy  Do not change redundancy Control system for redundancy Control timing structure Control interval –t c = t f C – period of control, where C – average number of the feedbacks from the peer –If C = 1 then root makes control decision at the average on each feedback interval 20

51. Mathematical Models for Streaming System Model of the Streaming Source –Model takes into account the root and leafs only (without aggregation packet loss rate measurements from other peers) –Error Vector takes into account the character of passing packets through network –There are S peer-leafs –Model of the streaming source with redundancy (Streaming Vector): Model of the streaming with feedback from leafs (simple case) P2P Streaming with feedback from leafs 21

52. Mathematical Models for Streaming System Model of the channels –Model of the channels from root to leafs (Estimation Vectors): –General model of the channels: Model of the streaming with feedback from leafs (simple case) Structure of P2P streaming network with feedbacks from leafs 22

53. Mathematical Models for Streaming System The network structure –Each peer measures packet loss rate (PLR) –Summarizes it with the PLR of its child –Send result and number of measurement to the parent –Stream source is unified for all peers (this is simplification) Model of the channels –General model of the channels Model of the streaming with feedback and aggregation of loss packet rates Structure of P2P streaming network with feedbacks and aggregation of loss rates 23

54. Mathematical Models for Streaming System Model of the channel taking account the FEC –The model of the channel (Estimation Vector) considered above took not into account the FEC procedure –Introducing of a Correction Vector will describe the FEC –The role of is to compensate the Error Vector –The compensation ability depends on redundancy (the more redundancy, the mere ability for Error Vector’s compensation) –Equation for the Estimation Vector: –The Vector depends on redundancy vector and it is defined as follow: where r and w are binary elements of redundancy and error vectors, respectively –Redundancy in the block will recover all lost packets if the weight of the Error Vector is equal or less than the weight of redundancy vector Model of the streaming with FEC 24 =

55. Estimation and Feedback control algorithms The PLR as indicator of the network state –Measurement of the network state is made by counting of loss packets in the measurement period –Packet Loss Rate indicator is Q –Two type of PLR are considered:  PLR before FEC (Q)  PLR after FEC (Q FEC ) –The Control Unit of peer receives one of this indicator and uses it for processing Packet Loss Rate (PLR) estimation 25 Error Vector as the presentation of the packet loss –The Error Vector: where –The weight of the Error Vector is the sum of its “1” elements:

56. Estimation and Feedback control algorithms The PLR before FEC –Sum of the weights of all Error Vectors in a measurement period is Packet Los Rate indicator: Packet Loss Rate (PLR) estimation 26 The PLR after FEC –FEC-redundancy recovers the lost packets –PLP after FEC ( Q FEC ) is difference between lost packets before FEC and packets recovered after FEC in the measurement interval –The Correction Vector: where –The weight of the Correction Vector is the sum of its “1” elements: –PLR after FEC is described sa follow: – –Estimation of the packet loss probability after FEC: –Estimation of the packet loss probability before FEC is defined as Q divided by number of all packets sent during measurement interval:

57. Estimation and Feedback control algorithms The two type of control system –Open-loop system  No feedbacks  Control unit is used to obtain desirable response –Close-loop system  The feedback is used  Measured output of system is compared with desired value  Control system affects to minimize the difference Control System (close-loop feedback) 27 The questions about the control algorithms –When the feedbacks must be sent? –When the system must react on the changing network state –How the system must react

58. Estimation and Feedback control algorithms On-off control method –The control system change redundancy in stepwise manner –Ste-by-step increment or decrement of the controller output (redundancy) –The max and min desirable thresholds are given beforehand Proportional method –The rounded up average number of the lost packets per block before FEC is evaluated Control System (close-loop feedback) 28 –The controller compares this estimation with the current redundancy –The difference is required number of the redundancy packets to add –The redundancy is defined as follow:

59. Estimation and Feedback control algorithms The control system with given target –The controller tries to make closer the channel state to the desired value –The proportional controller is used –Error of control e is the difference between desired packet loss probability p and estimated one –The main goal is to minimize e –Relation between the output ∆ R and input e is given by a proportional factor γ Control System (close-loop feedback) 29 –The input-output function is: ∆Rc+1 = γ · e c –The number of redundancy is defined as follow: R c +1 = R c + ∆R c +1 –This approach uses reaction of the control system for changing redundancy The proportional factor γ can be defined by simulations

60. Simulations (for the simple case) The conditions of the simulation –Only leafs send periodically feedback updates directly to the root –The root averages the updates and makes the decision on changing FEC redundancy –The stream rate is 160 kbps –The two cases are compared: 1. Fixed FEC 2. Adaptive FEC –The size of fixed block is 20 packets (16 for data and 4 for redundancy –The number of leafs is 20) –Feedback delay is 0 sec –Measurement interval the PLR and control interval are 5 sec (interval is 100 packets) –Given Packet Loss Probability is changed by SIN function from 0 to 0.5 –The simulation period is 5 min Case for the simulation 30

61. Simulations (for the simple case) The results of the simulation –The packet loss probability before FEC is shifted to right than given one –There is random deviation is because of inaccuracy of measurements –In general the packet and block loss probabilities after FEC for adaptive FEC are less than for fixed FEC –Adaptive changing redundancy reflects the work of the control system Case for the simulation 31

62. The questions for the research Update the mathematics for the mesh-based and network redundancy cases Introduce new algorithms Compare average (in time) loss probabilities for fixed and adaptive FEC cases Comparable performance evaluation both without redundancy and with constant redundancy: - dependencies of packet loss probability estimation on join and disjoin rate of nodes for case without FEC; - dependencies of packet loss probability estimation after FEC on layer of network for dif-ferent join and disjoin rate of nodes and redundancy; - dependencies of packet loss probability estimation after FEC on given packet loss prob-ability for different redundancy and layers of network; - other performances. Comparable performance evaluation both without redundancy and with variable (adaptive) redundancy: - dependencies of gain (ratio of packet loss probability after FEC with fixed and adaptive redundancy) on given packet loss probability with fixed measurement period; - dependencies of gain on measurement period with other fixed parameters; - dependencies of gain on number of nodes (layers of network) with other fixed parameters; - comparative QoS performances with taking account packet delay and feedback; - other performances. Considered cases for mesh-based and network redundancy models and algorithms: 32

63. Thank you