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Multimedia

Multimedia. Robert Grimm New York University. Content: Multimedia Overview. Multimedia = audio and video Saroiu et al.—An Analysis of Internet Content Delivery Systems How is multimedia distributed over the Internet? How much is there? MacCanne et al.—Receiver-Driven Layered Multicast

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Multimedia

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  1. Multimedia Robert Grimm New York University

  2. Content: MultimediaOverview • Multimedia = audio and video • Saroiu et al.—An Analysis of Internet Content Delivery Systems • How is multimedia distributed over the Internet? • How much is there? • MacCanne et al.—Receiver-Driven Layered Multicast • How to best stream multimedia across the Internet?

  3. Stefan Saroiu’s OSDI Talk

  4. Based on broadcast model One server, many clients Clients subscribe to streams Basic problem: Network heterogeneity One approach: Fixed rate, least common denominator Streaming Multimedia

  5. Better Approach:Layered Transmission Scheme • Basic idea: Encode signal in many layers • Each layer provides better quality • Sum of layers represents a session • Cumulative layers • Independent layers • Simulcast

  6. Three assumptions Best effort, multipoint packet delivery Efficiency of IP Multicast Group-oriented communication Important issue: router drop policy Underlying Network Model

  7. Basic control loop On congestion, drop a layer On space capacity, add a layer The RLM Protocol

  8. Capacity Inference • One option: Monitor link utilization in network • Problem: requires changes to network • Another option: Actively probe network • Join-experiments • Issues with join-experiments • Adaptability • Scalability

  9. Goal Perform infrequently when likely to fail Perform frequently when likely to succeed Algorithm Join-timer for each layer Exponential backoff for problematic layers Join-Experiment Adaptability

  10. Join-Experiment Adaptability(cont.) • How to correlate join-experiment with outcome? • Need to chose appropriate detection-time • Unknown • Variable • Use estimator • Initialize conservatively • Adjust based on failed join-experiments

  11. Join-Experiment Scalability • Issue: interaction of independent join-experiments • Add congestion • Interfere with each other • Approach: scale frequency with group size • But, what about convergence?

  12. Receiver notifies group of join-experiment On congestion, other receivers increase corresponding join-timer Conservative Local Join-Experiment ScalabilityShared Learning

  13. Join-experiments are not completely exclusionary Lower or equal level experiments may overlap What about router drop policy? More on Shared Learning

  14. Based on simulations (ns) Two metrics Worst-case short-term loss rate Convergence time to sustainablethroughput Four topologies Latency scalability Session scalability Bandwidth heterogeneity Superposition Evaluation

  15. Results • RLM • Is sensitive to transmission latency • Scales with group size • Though, convergence time increases! • Supports bandwidth heterogeneity • Though, with increased loss rate • Supports simultaneous sessions • Though, allocation was often unfair

  16. What Did You Learn Today? • Content distribution in the Internet • Receiver-driven layered multicast

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