Multipoint communication by hierarchically encoded data

Shacham, N.

doi:10.1109/infcom.1992.263483

Cited by 152 publications

(83 citation statements)

References 15 publications

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“…The solution gap of the combined objective values by the tabu search is within 6% even in tough cases with α = 0.9 and 0.8. ( 3,3,3,3,4,4,4,4,4,4,4,4,5,5,5,5,5,5,5,5,5,5,5,5,8,8,8,8,8,8) 1.518 1.631 Tabu ( 3,3,3,3,3,3,3,4,4,4,4,5,5,5,5,5,6,6,6,6,6,7,7,7,8,8,8,12,12,16 ) 1.492 1.623…”

Section: Bandwidth Allocation For the Dual Objectives (α < 1)mentioning

confidence: 99%

“…Tabu search ( 2,2,2,3,3,3,3,3,3,4,4,4,4,4,5,5,5,5,5,5,8,8,8,8,9,10,13,13,13,13 ) ( 2,2,2,3,3,3,3,3,3,4,4,4,4,4,5,5,5,5,5,5,8,8,8,8,…”

Section: Bandwidth Allocation For the Dual Objectives (α < 1)unclassified

“…To prevent the high overheads required for layered multicast a source needs to set the number of layers to transmit and assign bandwidth to each layer by organizing requirement by receivers [4].…”

Section: Introductionmentioning

confidence: 99%

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Discrete bandwidth allocation considering fairness and transmission load in multicast networks

Lee

Cho

2007

Computers & Operations Research

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As a promising solution to tackle the network heterogeneity in multicasting, layered multicast protocols such as Receiver-driven layered multicast (RLM) and Layered video multicast with retransmission (LVMR) have been proposed. This paper considers fairness as well as transmission load in the layered multicasting. Lexicographically fair bandwidth allocation among multicast receivers is considered under the constraint of minimum bandwidth requirement and the link capacity of the network. The problem of transmission load in the layer multicasting due to various user requirements is also examined by minimizing the number of layers.The bandwidth allocation is formulated as a nonlinear integer programming problem. A dual objective tabu search is proposed to solve the fairness and transmission load problem in multicast networks. Outstanding performance is obtained by the proposed tabu search. When the fairness objective is considered, the solution gap from the optimal solution is less than 2% in problems with 50 virtual sessions. The complexity of the dual objective largely depends on the weighting factor of the two objectives. Even in tough cases the proposed tabu search provides excellent solution the gap of which is within 6% from the optimal solution.

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Section: Bandwidth Allocation For the Dual Objectives (α < 1)mentioning

confidence: 99%

“…Tabu search ( 2,2,2,3,3,3,3,3,3,4,4,4,4,4,5,5,5,5,5,5,8,8,8,8,9,10,13,13,13,13 ) ( 2,2,2,3,3,3,3,3,3,4,4,4,4,4,5,5,5,5,5,5,8,8,8,8,…”

Section: Bandwidth Allocation For the Dual Objectives (α < 1)unclassified

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Discrete bandwidth allocation considering fairness and transmission load in multicast networks

Lee

Cho

2007

Computers & Operations Research

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“…The DBM AP with a priority arrival process can be particularly useful in modeling multimedia data traffic, especially for compressed video with layered encoding [15]. We are currently working on modeling MPEG-4 video data encoded in multiple layers by the DBM AP process with priorities [17], in which case one layer of the video data traffic is mapped to one priority level.…”

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confidence: 99%

A matrix-analytic solution for the DBMAP/PH/1 priority queue

Zhao¹,

Li²,

Cao

et al. 2006

Queueing Syst

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Priority queueing models have been commonly used in telecommunication systems. The development of analytically tractable models to determine their performance is vitally important. The discrete time batch Markovian arrival process (DBM AP ) has been widely used to model the source behavior of data traffic, while phase-type (P H) distribution has been extensively applied to model the service time. This paper focuses on the computation of the DBM AP/P H/1 queueing system with priorities, in which the arrival process is considered to be a DBM AP with two priority levels and the service time obeys a discrete P H distribution. Such a queueing model has potential in performance evaluation of computer networks such as video transmission over wireless networks and priority scheduling in ATM or TDMA networks. Based on matrix-analytic methods, we develop computation algorithms for obtaining the stationary distribution of the system numbers and further deriving the key performance indices of the DBM AP/P H/1 priority queue.

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“…In layered-video encoding/decoding [13,15,18], video data is encoded into a number of layers that can be sequentially combined to provide progressive quality refinement of the received video. In case of insufficient network bandwidth, the network can decide to drop some higher-layer data for some sets of users, or some receivers can choose not to receive some higher-layer data.…”

Section: Introductionmentioning

confidence: 99%

LVMSR-an efficient algorithm to multicast layered video

Wen¹,

Mukherjee

Ghosal

et al.

2000 IEEE International Conference on Communications. ICC 2000. Global Convergence Through Communications. Conference Record

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Layered video is a video-compression technique to encode video data in multiple layers. It typically consists of a base layer and some additional layers that provide enhanced video quality. The multicasting operation of layered video consists of many receivers dynamically joining and leaving different multicast sessions of different layers depending on their network condition. A layered video multicasting system needs to satisfy: (i) bounded end-to-end delay from the video source to each receiver; (ii) minimum total cost; and (iii) minimum delay jitter between the various video streams received by each receiver. The problem of computing such data distribution paths is NP-complete. This paper presents a new heuristic algorithm, called layered video multicast super-tree routing algorithm, with OðRn 2 Þ time complexity and OðR 2 Þ message complexity, where n is the number of nodes in the network and R is the receiver group size. Our investigation shows that the multicast data paths computed by our algorithm can always satisfy the delay constraint with reasonably low total cost.

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Multipoint communication by hierarchically encoded data

Cited by 152 publications

References 15 publications

Discrete bandwidth allocation considering fairness and transmission load in multicast networks

Discrete bandwidth allocation considering fairness and transmission load in multicast networks

A matrix-analytic solution for the DBMAP/PH/1 priority queue

LVMSR-an efficient algorithm to multicast layered video

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