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QoS-Based Multicast Routing for Distributing Layered Video to Heterogeneous Receivers in Rate-based Networks

QoS-Based Multicast Routing for Distributing Layered Video to Heterogeneous Receivers in Rate-based Networks. Bin Wang and Jennifer C.Hou. Goal: QoS requirements of heterogeneous receivers, including bandwidth and delay; Highest receiving quality for receivers

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QoS-Based Multicast Routing for Distributing Layered Video to Heterogeneous Receivers in Rate-based Networks

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  1. QoS-Based Multicast Routing for Distributing Layered Video to Heterogeneous Receivers in Rate-based Networks Bin Wang and Jennifer C.Hou

  2. Goal: • QoS requirements of heterogeneous receivers, including bandwidth and delay; • Highest receiving quality for receivers • Minimize the total network resource consumption Solution: • Source:Layered encoding(cummulative) • Receivers: tradeoff between video quality and available bandwidth • Scheduling: rate-based link scheduling • Tree construction on weighted digraph G=(V,E) using the global state and an auxiliary routing table

  3. Global state Link state: • Available bandwidth: b(l), b:ER+,the link bandwidth function, • Constant delay: dl,which depends on the capacity,the propagation delay, and the maximum packet size • Link cost Node state: available buffer… Global state: • The collection of the local node/link state of all the nodes in the network • Maintained by every node in the network

  4. The Auxiliary Routing Table T is a |V| X H matrix, recording a h-hop maximum bandwidth path : • P: path • bw:maximum bandwidth on P • Neighbour:next hop • dh=sum(dl): end-end constant delay *every node maintains a T

  5. Rate-based Scheduling Algorithms • Algorithms: Generalized Processor Sharing,Weighted Fair Queuing,Virtual Clock… • Traffic model: leaky bucket (R,sigma) • End-end delay bound on P: D(r,P)=(sigma+|P|*c)/r+sum(dl)

  6. Traffic Model for Layered Video • Each layer: leaky bucket(R,Sigma) • Video signal: (Ri,sigmai), i:1~m (#of layers) • Layer k: (Rk,sigmak), Rk=sumj=1k(Rj) sigmak=sumj=1k(sigmaj) • Layers are selectively forwarded on links

  7. Problem Formulation The one-to-many multicast video distribution session: s:source d={j|j=1~n}: receivers {Dj|j=1~n}:delay requirements {Rjr|j=1~n}:maximum acceptable rates, layer-k receiver j: Rk<=Rjr<Rk+1 How to construct a tree?

  8. Algorithm Overview • Starting from a tree with only s • Higher-layer receiver i first • Select the most appropriate path P from T • A setup message is sent to i along P, carrying the data structure RECEIVER and D(delay) • RECEIVER is updated by intermediate nodes, if better path is available • Next off-tree receiver j is selected by i • A fork message is sent from i • A finish message is sent to s if no off-tree node

  9. The RECEVIER data structure RECEIVER.RECEIVER[i] records the least-hop appropriate path P for receiver i: • OnTreeNode: initialized to s • path: P, with sufficient bandwidth • r: the minimum bandwidth ri for delay • cost: |P|*r, the total bandwidth due to receiver i(only for new branch) • Rr: maximum acceptable rate Rri • level: # of layers • tag: on-tree or off-tree

  10. Path Selection from T • Calculate the minimum bandwidth ri according to deley requirement: ri>=(sigmak+|p|*c)/(Di-sum(dl)) • Select the least-hop path with T(i,h).bw>=max(ri,Rk) • No loop • Reserved bandwidth: max(ri,Rk) • if no path exists,or ri>Rri, degrading layer (Lower cost? Best path?)

  11. Next Off-Tree Receiver Selection Higher layer & Smaller cost node first: • Gk+1=…=Gm=0, Gk<>0 • Select the receiver i from Gk with min(|P|*ri) • RECEIVER[i].tag=true • A setup message is sent to i • i will select next receiver j • i sends a fork message to RECEIVER[j].OnTreeNode

  12. Path Update Intermediate nodes update D&RECEIVER: • Delay requirement (D:cumulative delay) Di>=D+(sigmak+|p|*c)/ri+sum(dl) • Select the minimum-hop path P from T(first entry T(i,h)) • Smaller cost(total bandwidth): |P|*ri • Update RECEIVER for every receiver i if smaller cost

  13. Dynamic Receiver Join/Leave Goal: seamless transition via incremental changing Leave: • Leaf node: leave message is sent upstream,and resource is released by a fork node • Non-leaf node: just relay incoming downstream messages

  14. Dynamic Receiver Join/Leave(cond.) Join: • Join request to s with di&Rir • S multicasts a join message with RECEIVER[i]&D to all(?) on-tree receivers • Intermediate nodes updates D,and RECEIVER if smaller cost path available • The leaf receivers send back RECEIVER • S select a fork node with least cost • fork message (Why not use updated T? Least cost?)

  15. Auxiliary Routing Table T Update Compute the h-hop maximum bandwidth paths from the current node to all the other nodes:iterate H times, h=1~H • Update T(j,h)(j=1~|V): for every neighbour u of j, if no loop T(j,h).bw=max(T(j,h).bw,min(T(u,h-1).bw,b(u,j))) • If loop exists(j in T(u,h-1).P), recursively calculate a new T(u,h-1) excluding j • For complexity, excluding u if loop or limit the scope of recursion • Run off-line and infrequently

  16. Complexity • # of messages: O(2*|d|) • T update: exponential in the worst case If bapassing the loop: Check every neighbour u of j: O(|V|) Check loop and bw: O(H)+1 Run H times for every receiver: O(H)*O(|V|) So O(H2*|V|2)

  17. Simulation • Topology: vBNS , switch cluster,random network(Waxman method, which can obtain “real world” networks) • Simulator: NetSimQ • Comparing: maximum bandwidth tree algorithm Maxemchuk’s algorithm • Varing parameters: lambda(session arrival rate),|d|,Dj • Performance metrics: total bandwidth required, percentage of receivers attaining QoS

  18. Maxemchuk’s Algorithm • Minimize bandwidth consumption without considering QoS requirement • Use modified T-M heuristic • A variant of steiner tree problem: construct a minimum cost tree for a subset of nodes, with link cost fixed in the network • Link cost: basic cost *highest reserved rate • Construct from higher-rate receivers and then add lower-rate of receivers • No explicit QoS consideration • Centralization

  19. Max Bandwidth Tree Algorithm • For Layered-encoded data (cumulative) • Compute the maximum available bandwidth tree to connect all receivers,receivers are classified by receiving capabilities • Minimize the sum of satisfaction level • For shorter path:select the node nearest to source (not guarantee shortest path) • For bandwidth saving: reduce bandwidth from the receivers

  20. Simulation results

  21. Simulation results(cond)

  22. Simulation results(cond)

  23. Simulation results(cond)

  24. Issues: • The original Goal is achieved • Shortest path? Smallest total cost?The best path? • Complexity (scalability?): Global state T update Link state update Complexity! • A good attemption!

  25. A Better Solution?

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