Scheduling policies for an on-demand video server with batching
MULTIMEDIA '94 Proceedings of the second ACM international conference on Multimedia
Group-guaranteed channel capacity in multimedia storage servers
SIGMETRICS '97 Proceedings of the 1997 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Patching: a multicast technique for true video-on-demand services
MULTIMEDIA '98 Proceedings of the sixth 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)
The Maximum Factor Queue Length Batching Scheme for Video-on-Demand Systems
IEEE Transactions on Computers
Minimizing Bandwidth Requirements for On-Demand Data Delivery
IEEE Transactions on Knowledge and Data Engineering
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
Hi-index | 0.00 |
The number of video streams that can be serviced concurrently is highly constrained by the required real-time and high-rate transfers of multimedia data. Resource sharing techniques, such as Batching, Patching, and Earliest Reachable Merge Target (ERMT), can be used to address this problem by utilizing the multicast facility, which allows multiple requests to share the same set of server and network resources. They assume, however, that all clients have the same available download bandwidth and buffer space. We study how to efficiently support clients with varying available download bandwidth and buffer space, while delivering data in a client-pull fashion using enhanced resource sharing. In particular, we propose three hybrid solutions to address the variability in the download bandwidth among clients: Simple Hybrid Solution (SHS), Adaptive Hybrid Solution (AHS), and Enhanced Hybrid Solution (EHS). SHS simply combines Batching with either Patching or ERMT, leading to two alternatives: SHS-P and SHS-E, respectively. Batching is used for clients with bandwidth lower than double the video playback rate, and Patching/ERMT is used for the rest. In contrast, AHS and EHS classify clients into multiple bandwidth classes and service them accordingly. AHS employs a new stream type, called adaptive stream, and EHS employs an enhanced adaptive stream type to serve clients with bandwidth capacities ranging between the video playback rate and double that rate. AHS and EHS employ adaptive streams or enhanced adaptive streams in conjunction with Batching and Patching or ERMT, leading to four possible schemes: AHS-P, AHS-E, EHS-P, and EHS-E. Moreover, we consider the variability of the available buffer space among clients. Furthermore, we study how the waiting playback requests for different videos can be scheduled for service in the heterogeneous environment, capturing the variations in both the client bandwidth and buffer space. We evaluate the effectiveness of the proposed solutions and analyze various scheduling policies through extensive simulation.