Scheduling and bandwidth allocation for the distribution of archived video in VOD systems

Providing cost‐effective video‐on‐demand (VOD) services necessitates reducing the required bandwidth for transporting video over high‐speed networks. In this paper, we investigate efficient schemes for transporting archived MPEG‐coded video over a VOD distribution network. A video stream is characterized by a time‐varying traffic envelope, which provides an upper bound on the bit rate. Using such envelopes, we show that video streams can be scheduled for transmission over the network such that the per‐stream allocated bandwidth is significantly less than the source peak rate. In a previous work [13], we investigated stream scheduling and bandwidth allocation using global traffic envelopes and homogeneous streams. In this paper, we generalize the scheduling scheme in [13] to include the heterogeneous case. We then investigate the allocation problem under window‐based traffic envelopes, which provide tight bounds on the bit rate. Using such envelopes, we introduce three stream‐scheduling schemes for multiplexing video connections at a server. The performance of these schemes is evaluated under static and dynamic scenarios. Our results indicate a significant reduction in the per‐stream allocated bandwidth when stream scheduling is used. While this reduction is obtained through statistical multiplexing, the transported streams are guaranteed stringent, deterministic quality of service (i.e., zero loss rate and small, bounded delay). In contrast to video smoothing, our approach requires virtually no buffer at the set‐top box since frames are delivered at their playback rate.

[1]  David K. Y. Yau,et al.  An algorithm for lossless smoothing of MPEG video , 1994, SIGCOMM 1994.

[2]  S. SampathKumar,et al.  Technologies for distribution of interactive multimedia to residential subscribers , 1994, Proceedings of lst IEEE International Workshop on Community Networking.

[3]  James P. G. Sterbenz,et al.  Networking Requirements for Interactive Video on Demand , 1995, IEEE J. Sel. Areas Commun..

[4]  Michael Devetsikiotis,et al.  Modeling and simulation of self-similar variable bit rate compressed video: a unified approach , 1995, SIGCOMM '95.

[5]  Kevin C. Almeroth,et al.  The Use of Multicast Delivery to Provide a Scalable and Interactive Video-on-Demand Service , 1996, IEEE J. Sel. Areas Commun..

[6]  Wu-chi Feng,et al.  Smoothing and buffering for delivery of prerecorded compressed video , 1995, Electronic Imaging.

[7]  Ness B. Shroff,et al.  Video modeling within networks using deterministic smoothing at the source , 1994, Proceedings of INFOCOM '94 Conference on Computer Communications.

[8]  Edward W. Knightly,et al.  Traffic characterization and switch utilization using a deterministic bounding interval dependent traffic model , 1995, Proceedings of INFOCOM'95.

[9]  Donald F. Towsley,et al.  Supporting stored video: reducing rate variability and end-to-end resource requirements through optimal smoothing , 1998, TNET.

[10]  Edward W. Knightly,et al.  Fundamental limits and tradeoffs of providing deterministic guarantees to VBR video traffic , 1995, SIGMETRICS '95/PERFORMANCE '95.

[11]  Satish K. Tripathi,et al.  Impact of video scheduling on bandwidth allocation for multiplexed MPEG streams , 1997, Multimedia Systems.

[12]  Wu-chi Feng,et al.  An optimal bandwidth allocation strategy for the delivery of compressed prerecorded video , 1997, Multimedia Systems.

[13]  Ali Tabatabai,et al.  A scheme for smoothing delay-sensitive traffic offered to ATM networks , 1992, [Proceedings] IEEE INFOCOM '92: The Conference on Computer Communications.

[14]  Dipankar Raychaudhuri,et al.  Bandwidth Renegotiation for VBR Video Over ATM Networks , 1996, IEEE J. Sel. Areas Commun..

[15]  Walter Willinger,et al.  Analysis, modeling and generation of self-similar VBR video traffic , 1994, SIGCOMM.

[16]  Martin Reisslein,et al.  Join { the { Shortest { Queue Prefetching for VBR Video on Demand 1 , 1997 .

[17]  Mark W. Garrett,et al.  Modeling and generation of self-similar vbr video traffic , 1994, SIGCOMM 1994.

[18]  Martin Vetterli,et al.  Congestion control strategies for packet video , 1991 .

[19]  Marwan Krunz,et al.  Statistical characteristics and multiplexing of MPEG streams , 1995, Proceedings of INFOCOM'95.

[20]  Eric Wing Ming Wong,et al.  Performance Model of Interactive Video-on-Demand Systems , 1996, IEEE J. Sel. Areas Commun..

[21]  Hui Zhang,et al.  Traac Characterization and Switch Utilization Using a Deterministic Bounding Interval Dependent Traac Model , 1994 .

[22]  Keith W. Ross,et al.  A join-the-shortest-queue prefetching protocol for VBR video on demand , 1997, Proceedings 1997 International Conference on Network Protocols.

[23]  Amy R. Reibman,et al.  Traffic descriptors for VBR video teleconferencing over ATM networks , 1995, TNET.

[24]  Keith W. Ross,et al.  Video-on-Demand Over ATM: Constant-Rate Transmission and Transport , 1996, IEEE J. Sel. Areas Commun..

[25]  Marcel Graf VBR video over ATM: reducing network resource requirements through endsystem traffic shaping , 1997, Proceedings of INFOCOM '97.

[26]  H.M. Vin,et al.  Designing an on-demand multimedia service , 1992, IEEE Communications Magazine.