Constrained Consonant Broadcasting- A Generalized Periodic Broadcasting Scheme for Large Scale Video Streaming W. C. Liu and Jack Y. B. Lee Department.

Slides:



Advertisements
Similar presentations
Class-constrained Packing Problems with Application to Storage Management in Multimedia Systems Tami Tamir Department of Computer Science The Technion.
Advertisements

Continuous Media 1 Differs significantly from textual and numeric data because of two fundamental characteristics: –Real-time storage and retrieval –High.
Scalable On-demand Media Streaming Anirban Mahanti Department of Computer Science University of Calgary Canada T2N 1N4.
Multimedia Systems As Presented by: Craig Tomastik.
Chapter 20: Multimedia Systems Silberschatz, Galvin and Gagne ©2005 Operating System Concepts Chapter 20: Multimedia Systems What is Multimedia.
Optimization of Data Caching and Streaming Media Kristin Martin November 24, 2008.
A simple model for analyzing P2P streaming protocols. Seminar on advanced Internet applications and systems Amit Farkash. 1.
Silberschatz, Galvin and Gagne ©2009 Operating System Concepts – 8 th Edition Chapter 20: Multimedia Systems.
Slice–and–Patch An Algorithm to Support VBR Video Streaming in a Multicast– based Video–on–Demand System.
Scalable On-demand Media Streaming with Packet Loss Recovery Anirban Mahanti Department of Computer Science University of Calgary Calgary, AB T2N 1N4 Canada.
A Server-less Architecture for Building Scalable, Reliable, and Cost-Effective Video-on-demand Systems Jack Lee Yiu-bun, Raymond Leung Wai Tak Department.
Efficient and Flexible Parallel Retrieval using Priority Encoded Transmission(2004) CMPT 886 Represented By: Lilong Shi.
June 3, 2015Windows Scheduling Problems for Broadcast System 1 Amotz Bar-Noy, and Richard E. Ladner Presented by Qiaosheng Shi.
Harmonic Broadcasting for Video-on- Demand Service Enhanced Harmonic Data Broadcasting And Receiving Scheme For Popular Video Service Li-Shen Juhn and.
1 A Comparative Study of Periodic Broadcasting Scheme for Large-Scale Video Streaming Prepared by Nera Liu.
Client Buffering Techniques for Scalable Video Broadcasting Over Broadband Networks With Low User Delay S.-H. Gary Chan and S.-H. Ivan Yeung, IEEE Transactions.
1 A Low Bandwidth Broadcasting Protocol for Video on Demand J. Paris, S. W. Carter, D. D. E. Long In Proceedings of ICCCN, 1998.
1 Adaptive Live Broadcasting for Highly-Demanded Videos Hung-Chang Yang, Hsiang-Fu Yu and Li-Ming Tseng IEEE International Conference on Parallel and Distributed.
Analysis of Using Broadcast and Proxy for Streaming Layered Encoded Videos Wilson, Wing-Fai Poon and Kwok-Tung Lo.
VCR-oriented Video Broadcasting for Near Video-On- Demand Services Jin B. Kwon and Heon Y. Yeon Appears in IEEE Transactions on Consumer Electronics, vol.
Seamless Channel Transition for Pyramid- based Near-VOD Services Student: Wei-De Chien Advisor: Prof. Ja-Shung Wang.
An Active Buffer Management Technique for Providing Interactive Functions in Broadcast Video-on-Demand Systems Zongming Fei, Member, IEEE, Mostafa H. Ammar,
Scalable On-Demand Media Streaming With Packet Loss Recovery Anirban Mahanti, Derek L. Eager, Mary K. Vernon, and David J. Sundaram-Stukel IEEE/ACM Trans.
Prefix Caching assisted Periodic Broadcast for Streaming Popular Videos Yang Guo, Subhabrata Sen, and Don Towsley.
HHMSM: A Hierarchical Hybrid Multicast Stream Merging Scheme For Large-Scale Video-On-Demand Systems Hai Jin and Dafu Deng Huazhong University of Science.
Distributed Multimedia Streaming over Peer-to-Peer Network Jin B. Kwon, Heon Y. Yeom Euro-Par 2003, 9th International Conference on Parallel and Distributed.
A Novel Video Layout Strategy for Near-Video-on- Demand Servers Shenze Chen & Manu Thapar Hewlett-Packard Labs 1501 Page Mill Rd. Palo Alto, CA
A Fixed-Delay Broadcasting Protocol for Video-on-Demand Jehan-Francois Paris Department of Computer Science University of Houston A Channel-Based Heuristic.
Optimal Proxy Cache Allocation for Efficient Streaming Media Distribution Bing Wang, Subhabrata Sen, Micah Adler, and Don Towsley INFOCOM 2002.
A Server-less Architecture for Building Scalable, Reliable, and Cost-Effective Video-on-demand Systems Presented by: Raymond Leung Wai Tak Supervisor:
Performance Evaluation of Peer-to-Peer Video Streaming Systems Wilson, W.F. Poon The Chinese University of Hong Kong.
Fast broadcasting scheme(FB) In FB scheme, we divide a movie into 2 k - 1 segments, k channels is needed. S = S 1 · S 2 · S 3 · S 4 · S 5 · S 6 · S 7 Waiting.
Scalable Live Video Streaming to Cooperative Clients Using Time Shifting and Video Patching Meng Guo and Mostafa H. Ammar INFOCOM 2004.
Limiting the client bandwidth of broadcasting protocols for video on demand Jehan-Francois Paris and Darrell D.E. Long Proceedings of the Euromedia 2000.
“On the Integration of MPEG-4 streams Pulled Out of High Performance Mobile Devices and Data Traffic over a Wireless Network” Spyros Psychis, Polychronis.
A scalable technique for VCR-like interactions in video-on-demand applications Tantaoui, M.A.; Hua, K.A.; Sheu, S.; IEEE Proceeding of the 22nd International.
Design of an Interactive Video- on-Demand System Yiu-Wing Leung, Senior Member, IEEE, and Tony K. C. Chan IEEE Transactions on multimedia March 2003.
Schemes for Video on demand Yuan-Shiang Yeh. Outline Introduction Previous Works Study Buffer Requirement Channel Adjustment Bandwidth reduction in multi-layer.
CS Spring 2012 CS 414 – Multimedia Systems Design Lecture 34 – Media Server (Part 3) Klara Nahrstedt Spring 2012.
A Server-less Architecture for Building Scalable, Reliable, and Cost-Effective Video-on-demand Systems Raymond Leung and Jack Y.B. Lee Department of Information.
E0262 MIS - Multimedia Playback Systems Anandi Giridharan Electrical Communication Engineering, Indian Institute of Science, Bangalore – , India.
XE33OSA Chapter 20: Multimedia Systems. 20.2XE33OSA Silberschatz, Galvin and Gagne ©2005 Chapter 20: Multimedia Systems What is Multimedia Compression.
CPSC 441: Multimedia Networking1 Outline r Scalable Streaming Techniques r Content Distribution Networks.
Segment-Based Proxy Caching of Multimedia Streams Authors: Kun-Lung Wu, Philip S. Yu, and Joel L. Wolf IBM T.J. Watson Research Center Proceedings of The.
Silberschatz, Galvin and Gagne ©2009 Operating System Concepts – 8 th Edition, Multimedia Systems.
Storing and Serving Multimedia. What is a Media Server? A scalable storage manager Allocates multimedia data optimally among disk resources Performs memory.
A simple model for analyzing P2P streaming protocols. Seminar on advanced Internet applications and systems Amit Farkash. 1.
Simulation case studies J.-F. Pâris University of Houston.
Managing VBR Videos. The VBR Problem Constant quality Burstiness over multiple time scales Difference within and between scenes Frame structure of encoding.
Minimum Cost Scheduling of Stored Video in Dynamic Bandwidth Allocation Networks Reporter : M 張益瑞 IEEE Transactions on Consumer Electronics, Vol.
Large-Scale and Cost-Effective Video Services CS587x Lecture Department of Computer Science Iowa State University.
Scalable video distribution techniques Laurentiu Barza PLANETE project presentation: Sophia Antipolis 12 October 2000.
1 Scheduling Techniques for Broadcasting Popular Media. Amotz Bar-Noy Brooklyn College Richard Ladner Tami Tamir University of Washington.
An Empirical Study on 3G Network Capacity and Performance INFOCOM2007 Wee Lum Tan, Fung Lam and Wing Cheong Lau Chinese University.
Cost-Effective Video Streaming Techniques Kien A. Hua School of EE & Computer Science University of Central Florida Orlando, FL U.S.A.
Chapter 20: Multimedia Systems
Multimedia Systems Operating System Presentation On
DASH2M: Exploring HTTP/2 for Internet Streaming to Mobile Devices
Chapter 20: Multimedia Systems
The Impact of Replacement Granularity on Video Caching
Video on Demand (VoD) March, 2003
A Server-less Architecture for Building Scalable, Reliable, and Cost-Effective Video-on-demand Systems Raymond Leung and Jack Y.B. Lee Department of Information.
Video On Demand.
Video Summarization by Spatial-Temporal Graph Optimization
Chapter 20: Multimedia Systems
Chapter 20: Multimedia Systems
Chapter 20: Multimedia Systems
Chapter 20: Multimedia Systems
Presentation transcript:

Constrained Consonant Broadcasting- A Generalized Periodic Broadcasting Scheme for Large Scale Video Streaming W. C. Liu and Jack Y. B. Lee Department of Information Engineering The Chinese University of Hong Kong ICME 2003

Outline Poly-harmonic Broadcasting Constrained-Consonant Broadcasting Performance Evaluation

Objective CCB can be considered as a generalization of the Poly-harmonic Broadcasting scheme incorporating two important constraints, namely client access bandwidth and client buffer requirements.

Notations

L B S1S1 S1S1 S1S1 S1S1 S2S2 S3S3 SNSN

Harmonic Broadcasting (HB) Divide a video into N equally-sized segments Each segment S i, for 1 ≤ i ≤ N, is broadcast repeatedly on its own channel with a bandwidth ( b/i ) HB does not always deliver all data on time

Harmonic Broadcasting –The i th segment of the movie S i is equally divided into i sub-segment( s ) { S i, 1, S i, S i, i } –Let the i sub-segment(s) of S i be put on a logical channel C i, the bandwidth of C i is b / i.

Harmonic Broadcasting The total bandwidth(B) allocated for the movie is as follows: Where H N is called the harmonic number of N B = b + b/2 + b/3 + b/4 = 2.083b H N = 1 + 1/2 + 1/3 + 1/4 = 2.083

An illustration of the first three streams for a video under harmonic broadcasting Play Rate : b Receive Rate : b/2

Poly-harmonic Broadcasting Two major changes: 1) The client STB starts downloading data from the moment a customer requests a specific video instead of waiting until the customer begins watching the beginning of the first segment. 2) Fixed wait policy.

Poly-harmonic Broadcasting Divide the video into N equal segments of duration U =( L / N ) Segment Si at a transmission rate –B i = b / ( m + i - 1) No client can start consuming the S 1 of the video before having downloaded data from all N streams during a time interval of duration T = mU, m is an integer ≧ 1. Segment S i will not be consumed until ( m + i –1) U time units have elapsed.

Poly-harmonic Broadcasting ( m =2)

Poly-harmonic Broadcasting The Bandwidth –If N = k * m Since T = mU; U= L/N; T = L/k ; k 愈大 則 waiting time 愈小

Poly-harmonic Broadcasting (PHB) Unlike the original HB, the Poly-harmonic Broadcasting scheme guarantees continuous video playback and at the same time can achieve near- optimal performance. Provides the same maximum waiting time as harmonic broadcasting protocol while consuming significantly less bandwidth.

Poly-harmonic Broadcasting Poly-harmonic Broadcasting requires a client to be able to receive all broadcasting channels simultaneously and has a buffer large enough to store up to 37% of the whole video.  difficult to implement!

Constrained-Consonant Broadcasting (CCB) Divide the video into N equal segments of duration U =( L / N ) Target latency T = mL/N=mU Classify broadcasting channels into two types, namely Type-I and Type-II channels.

Type-I Channels Channels are allocated with progressively less bandwidth as given by for the i th channel, where n 1 is the total number of Type-I channels. For Type-I channels, the client is required to start receiving video segments upon entering the system and begin video playback in T seconds. For m =2, B0 = b/2, B1 = b/3, B2 = b /4…

Type-I Channels Solve for n 1, such that the following constraints are satisfied: if we remove both the client access bandwidth and client buffer constraints  n 1 = N, CCB  PHB This PHB can be considered as a special case of CCB without client access bandwidth and client buffer constraints. (a) (b)

Type-II Channels Type-II channels are divided into groups of consecutive channels. Once a client completes receiving a video segment, the corresponding channel will be released ---begin receiving a new group of Type-II channels.

Type-II Channels Let n 2,j be the number of channels in group j, of which is created after channel j is released, where j =0,1,…, etc. Then the bandwidth allocation for channels in group j is given by BjBj BiBi

Type-II Channels Solve for n 2, such that the following constraints are satisfied: (c) (d) and where

Performance Evaluation