1. Differentiated Quality Video Delivery in Overlay Multicasting Environment
Ying Qiao
Carleton University
Project Presentation at the class:
Quality of Service Management for Multimedia Applications
Provided by: Professor Bochmann
12/6/2018

2. Outline Introduction Overlay multicast environment
-- Internet multimedia delivery
-- Types of Video service
-- multimedia multicast
Overlay multicast environment
-- Video coding
-- Video delivery
Layered Peer-to-Peer Streaming
Supporting Large-Scale Live Streaming Applications with Dynamic Application End-Points
Incentive mechanism for Peer-to-Peer Media Streaming
Conclusion
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3. Introduction (1) Internet media delivery Types of Video Service
-- No VOD
-- Pay-Per-view
-- True VOD
-- Near VOD (NVOD)
-- Quasi-VOD (QVOD)
Basic multicast functionality
-- Group membership management
-- Data delivery path maintenance
-- Replication and forwarding
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4. Introduction (2) Internet media multicast IP multicast
Overlay multicast
Ref 4
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5. Overlay Multicasting Environment (1)
Resources provided by peer end node
-- Network bandwidth
-- Storage space
-- CPU power
Features
-- Overlay Multicast is deployed with the basic uni-cast routing infrastructure
-- End hosts only maintain state for the groups they are participating in
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6. Overlay Multicasting Environment (2)
Three architectures
-- Dedicated-Infrastructure
-- Application-Endpoint
-- Waypoint
[Ref 4]
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7. Overlay Multicasting Environment (3)
Video Coding
-- Replicated streaming
-- Layered streaming
-- Multiple Description Coding
[Ref 1]
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8. Overlay Multicasting Environment (4)
Video Delivery tree
-- Single tree
-- Multiple tree
ZIGZAG [Ref 5]
SplitStream [Ref 6]
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9. Overlay Multicasting Environment (5)
Challenge for overlay multicast
-- Bandwidth constraints
-- Receiver scalability
-- Network dynamics
-- Receiver heterogeneity
[Ref 4]
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10. Layered Peer-to-Peer Streaming (1)
Layered video [Ref 2]
-- Video is encoded into one base layer and multiple enhancement layers
-- The base layer can be decoded independently
-- The enhancement layers can be decoded cumulatively
Network heterogeneity
[Ref 3]
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11. Layered Peer-to-Peer Streaming (2)
Large-scale on-demand multimedia distribution
-- Asynchrony of user requests
-- Heterogeneity of client resource capabilities
Layered Peer-to-Peer Streaming
-- Cache-and-relay
-- Layer-encoded streaming
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12. Layered Peer-to-Peer Streaming (3)
-- Cache-and-relay
-- Layer-encoded streaming
Goal
-- Maximize the number of the received streams from end nodes other than the source
-- Subject to
(1) number of received streams for one receiver <= inbound bandwidth of the receiver
(2) total number of received streaming from one sender <= outbound bandwidth of the sender
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13. Basic Algorithm Receiver k, inbound bandwidth
a set of the hosts qualified as the supplying peers of and sorted the Hosts with the available layers
Arranging the layers from the beginning of S
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14. Performance Evaluation (1)
Request composition:
-- Modem/ISDN peers, 50%, 112kbps
-- Cable Modem/DSL peers, 35%, 1Mbps
-- Ethernet Peers, 15%, 10Mbps
Quality satisfaction
-- The ratio of received quality and expected quality of a peer
Result
--The layered approach is able to fully utilize the marginal outbound bandwidth of supplying peer, and more adapted to the bandwidth asymmetric
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15. Performance Evaluation (2)
Longer buffer enables a supplying peer to help more later-coming peers by prolonging the supplying chain
Further increasing buffer size has very little help at prolonging the supplying chain
Request chain (tree) in both cases
Layered approach relieves the server bandwidth request with peer bandwidth
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16. Fairness Outbound/inbound < 1
40% Ethernet Peers are not fully satisfied
Reason: the limiting inbound of the Modem/ISDN, and Cable Modem/DSL peers can not satisfied the Ethernet Peers
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17. Robustness Robustness
-- 50% of the supplying peers depart early before the playback is finished
-- Reconfiguration through buffer
-- Failure ratio is the percentage of failed peers among all departure peers
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18. Conclusion for the layered Peer-To-Peer Streaming
Be optimal at maximizing the streaming quality of heterogeneous peers
Be scalable at saving server bandwidth
Be efficient at utilizing bandwidth resource of supplying peers
Evaluation
-- Whether establishing fairness among peers, in terms of streaming quality satisfaction and bandwidth contribution
-- Whether being robust against unexpected peer departures/failures
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19. Supporting Large-Scale Live Streaming Applications
Key requirements
-- Resource constraints
-- Stability
-- Efficient overlay structure
Live Streaming Workload
-- Large scale: the peak group size is 1,000 to 80,000 hosts
-- A large number of short participations
-- Heavy tail with some very long participations
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20. Bandwidth Resource Constraints
Single Tree Protocols
-- Resource Index:
-- Trace study shows sufficient bandwidth resource
Multiple Tree Protocol
-- Increase the overall resilience
-- Tightly coupled with specialized video encoding
-- Resource Index: SupplyOfBW/DemandOfBW
-- Increase the supply of the resources
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21. Stability (1) Metrics Simulation of single tree Simulation Results
-- Mean interval between ancestor change for each participation Number of descendants of a departing participation
Simulation of single tree
-- Host join: asks the source to get m current group members, picks one host as parent
-- Host leave: all of its descendants pick one host
-- Parent Selection Algorithms: Oracle; Longest-First; Minimum depth; Random
Simulation Results
-- Oracle is the best
-- Minimum depth tree can provide good performance
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22. Stability (2) Simulation Results -- Oracle is the best
-- Minimum depth tree can provide good performance
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23. Stability (3) Impact of Multiple-Tree Protocols Simulation result
-- Independent trees
-- Load balancing
-- Preemption
Simulation result
-- More frequent ancestor changes
-- Improved performance comes at a cost of more frequents disconnects, more protocol overhead, and more complex protocols
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24. Efficient overlay structure (1)
Overlay structure closely reflects the underlying IP network
-- Need to discover other nearby hosts as parents
-- Partition hosts into clusters
-- One member of each cluster is designated as the clustered head
-- Hosts in the same cluster maintain knowledge about one another
Clustering Quality Metric
-- Average and maximum intra-cluster distance in milliseconds
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25. Efficient overlay structure (2)
Sensitivity to Number of Clusters
-- More clusters smaller intra-cluster distance
-- Maximum intra-cluster distance more sensitive to the change of number of clusters
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26. Efficient overlay structure (3)
Sensitivity to Cluster Size and Resource Maintenance
-- Bounding the cluster size doesn’t significantly affect the intra-cluster distances
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27. Conclusion for large-scale live streaming applications with dynamic application end-points
Minimizing depth in single-tree protocols provides good stability performance
Multiple-tree protocols can significantly improve the quality of streams
Simple clustering techniques improve the efficiency of the overlay structure
Opening issue: encourage application end-points to contribute their resources is an important direction
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28. Incentive Mechanism for Peer-to-Peer Media Streaming (1)
System quality is:
T is the total number of the packets in a streaming session, is 1 if the packet i arrives at the receiver before its scheduled play-out time, and 0 otherwise
Cooperation brings quality
Simultaneous uploading hurts quality
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29. Incentive Mechanism for Peer-to-Peer Media Streaming (2)
Random peer selection provides random quality
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30. Score-based incentive mechanism
Peer selection scheme allows a user to select peers with equal or lower rank to serve as suppliers
A user wishes to receive better-than-best-effort streaming, it must earn a positive score by contributing to the system
The stream quality for a receiver can be expressed as a function of contribution, score, or rank
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31. Functions Scoring function: could be: Contribution cost:
Rank Computation:
Quality function:
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32. Experiment system
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33. Performance evaluation
Expected rate: the total bytes coming from all senders
The gain increases for the incentive when the K increases
When k>20, the difference of the rates decreases because the bottleneck is shifted from the hosts to the network
Packets the miss their play-out deadlines are considered as lost
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34. Quality of Streaming
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35. Conclusion for incentive mechanism for Peer-to-Peer Media Streaming
Motivation
-- The stream quality is poor if the level of cooperation is low
-- Cooperation from a few altruistic users cannot provide high quality streaming to its users in a large system
Conclusion
-- A rank-based incentive mechanism achieves cooperation through service differentiation
-- The contribution of a user is converted into a score, then the score is mapped into a rank, and the rank provides flexibility in peer selection that determines the quality of a streaming session
-- Cooperative users earn higher rank by contributing their resources to others, and eventually receive high quality streaming
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36. Conclusion Application layer multicasting
Consuming the other end node’s resource while sharing own resource out
The differentiated quality is realized with replicated streaming, layered streaming, and MDC
Replicated streaming is used at the single tree delivery
In the single tree, the minimize depth algorithm shows good performance
Layered Streaming and MDC with multiple tree delivery increases resource, and improve the stability as well
Cluster can improve the efficiency of the overlay structure
Fairness is still an open issue
Incentive mechanism is a solution to encouraging resource sharing
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37. Reference [1] Layered Peer-to-Peer Streaming
[2] A Comparison of Layering and Stream Replication Video Multicast Schemes
[3] Receiver-Driver layered Multicast
[4] Internet Multicast Video Delivery
[5] ZIGZAG: An Efficient Peer-to-Peer Scheme for Media Streaming
[6] SplitStream: High-bandwidth content distribution in cooperative environment
[7] The Feasibility of Supporting Large-Scale Live Streaming Applications with Dynamic Application End-Points
[8] Incentive Mechanism for Peer-to-Peer Media Streaming
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38. Appendix: Receiver-Driven Layered Multicast
Rate-adaptation protocol
Each receiver runs the control loop:
-- On congestion, drop a layer
-- On spare capacity, add a layer
Join-experiment
-- adding layers at “well-chosen” times
-- causing congestion, then the receiver drops the adding layers
-- successful, the receiver start adding another join-experiment
Exponential Join timer for RLM adaptation at the join experiment
“Sharing learning” in multiple receivers for scaling of the receiver
12/6/2018