Scheduling policies for an on-demand video server with batching
MULTIMEDIA '94 Proceedings of the second ACM international conference on Multimedia
Optimal and efficient merging schedules for video-on-demand servers
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
An efficient bandwidth-sharing technique for true video on demand systems
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
Sharing Multicast Videos Using Patching Streams
Multimedia Tools and Applications
Loss-resilient on-demand media streaming using priority encoding
Proceedings of the 12th annual ACM international conference on Multimedia
Best-Effort Patching for Multicast True VoD Service
Multimedia Tools and Applications
Scalable media streaming to interactive users
Proceedings of the 13th annual ACM international conference on Multimedia
Analysis of Resource Sharing and Cache Management in Scalable Video-on-Demand
MASCOTS '06 Proceedings of the 14th IEEE International Symposium on Modeling, Analysis, and Simulation
Scalable streaming for heterogeneous clients
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Towards scalable delivery of video streams to heterogeneous receivers
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Analysis of waiting-time predictability in scalable media streaming
Proceedings of the 15th international conference on Multimedia
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Motivated by the impressive performance of cost-based scheduling for media streaming, we investigate its effectiveness in detail and analyze opportunities for further tunings and enhancements. Guided by this analysis, we propose a highly efficient enhancement technique that optimizes the scheduling decisions to increase the number of requests serviced concurrently and enhance user-perceived quality-of-service. We also analyze the waiting-time predictability achieved by the new technique. The simulation results show that it can achieve significant performance improvements while providing users with highly accurate expected waiting times.