1. EE689 Lecture 14 Review of Last lecture Receiver-driven Layered Multicast

2. Receiver-driven Multicast Sender based schemes don’t scale well as number of receivers increase Receiver based schemes scale better Receivers can decide the level of reliability needed - level of quality desired etc. Receivers can coordinate and reduce the work for loss recovery

3. Layered Multicast Receivers and network connectivity heterogeneous –Difficult to deal with packet losses –Receivers with higher connectivity should be allowed to get a better quality picture –Receivers with lower connectivity can get better quality through layered video than random losses of packets in flat video

4. RLM RLM - developed at LBL Café Mocha - at TAMU Use layered video coding Allow receivers to subscribe to different layers Organize multicast into several groups - each group corresponds to a different layer

5. Network Congestion Network congestion - dynamic With higher traffic, multicast should backoff to lower layers With less traffic, could get higher quality Have to allow receivers to drop/add layers dynamically Add/Join Experiments

6. Add-Join Experiments Receivers add a layer to see if there is enough BW If packet losses go up, then experiment unsuccessful - don’t add layer Associate a timer with each layer On a failure, double the timer for next join experiment

7. Join Experiments On unsuccessful joins, all the receivers on the same subnet could learn from failure Increase everyone’s timer on a failed expt. If everyone tries an experiment at the same time, –results can be different than individual attempts –can cause increased traffic

8. Join Experiments Add a random component to join experiments If a join experiment is in progress, don’t initiate another one –multicast a join experiment –allows shared learning, increase your timer on someone else’s failed experiment –convergence time can be high –priorities in network affect RLM scalability

9. Simultaneous joins Simultaneous join experiments may happen on different layers If experiment fails –join experiment at the highest layer => failed –join experiment a a lower layer => not sure, could be due to other experiment Start a Measurement mode to determine if long-term congestion persists.

10. States of RLM Steady state (S), Drop state (D), Measurement state (M) and Hysteresis (H) Hysteresis state allows not reacting to transient congestion caused by join experiments –use a detection timer T

11. Issues Latency Scalability Session Scalability Bandwidth Heterogeneity Superposition

12. Network Implications Receiver Consensus –One bad user can cause trouble for subnet Group Maintenance –add/join should happen quickly (IGMP) Fairness –multiple multicasts -how to share BW –how to ensure multicast doesn’t hog BW

13. TCP-friendly Multicast Arrange layers in an exponentially increasing data rates In steady state, packet drop => congestion, drop a layer –If layers are doubling in data rates, dropped layer = reducing multicast rate by half => TCP friendly

14. Summary Receiver-driven layered multicast works Critically dependent on timers Much work is being done to make many of these mechanisms more efficient Need for reducing overhead –add/join experiments