NUM-Based Fair Rate-Delay Balancing for Layered Video Multicasting over Adaptive Satellite Networks

Pradas, David; Vázquez-Castro, M. A.

doi:10.1109/jsac.2011.110507

Cited by 22 publications

(18 citation statements)

References 23 publications

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“…Though not exactly the same because of the different deployment under consideration, this policy is analogous to a weighted-round-robin policy is which the weights are determined based on both the rate and the delay requirements. Because our proposal falls into this category of approaches that do not require packet-level information, and to better illustrate the differences with prior works, we provide an intuitive comparison of our resource allocation with two benchmark policies (which we call "R-Round-Robin" and "RD-Round-Robin") that encapsulate the merits of Dutta et al [19] and Pradas and Vazquez-Castro [20], respectively. It is important to note that these policies are stationary because the allocation of the current slot does not depend on, and adapt to, the allocation of previous slots within each slot allocation block.…”

Section: Review Of State-of-the-art In Video Streaming Over Wirelementioning

confidence: 99%

“…• (i) round-robin slot assignment ("Round-robin"), which is the most typical option in WPAN MAC designs [1][2] [7][8]; • (ii) rate-proportional round-robin slot assignment ("R-Round-robin") [19], where the average bit-budget of each sensor within each RAB is used to allocate a proportional number of slots in a round-robin fashion (higher budget corresponds to more slots for a sensor); • (iii) rate/delay-proportional round-robin slot assignment ("RD-Round-robin") [20], where, beyond the bit-budget, the delay deadline of each sensor is also used to weight the slot assignment according to a heuristic rule, i.e., beyond rate, the delay deadline is taken into account in the proportional slot allocation. Note that the optimal slot allocation ("Optimal"), i.e.…”

Section: A Benchmarksmentioning

confidence: 99%

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Non-Stationary Resource Allocation Policies for Delay-Constrained Video Streaming: Application to Video over Internet-of-Things-Enabled Networks

Andrepoulos

Xiao

et al. 2014

IEEE J. Select. Areas Commun.

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Due to the high bandwidth requirements and stringent delay constraints of multi-user wireless video transmission applications, ensuring that all video senders have sufficient transmission opportunities to use before their delay deadlines expire is a longstanding research problem. We propose a novel solution that addresses this problem without assuming detailed packet-level knowledge, which is unavailable at resource allocation time (i.e. prior to the actual compression and transmission). Instead, we translate the transmission delay deadlines of each sender's video packets into a monotonically-decreasing weight distribution within the considered time horizon. Higher weights are assigned to the slots that have higher probability for deadline-abiding delivery. Given the sets of weights of the senders' video streams, we propose the low-complexity Delay-Aware Resource Allocation (DARA) approach to compute the optimal slot allocation policy that maximizes the deadline-abiding delivery of all senders. A unique characteristic of the DARA approach is that it yields a non-stationary slot allocation policy that depends on the allocation of previous slots. This is in contrast with all existing slot allocation policies such as roundrobin or rate-adaptive round-robin policies, which are stationary because the allocation of the current slot does not depend on the allocation of previous slots. We prove that the DARA approach is optimal for weight distributions that are exponentially decreasing in time. We further implement our framework for real-time video streaming in wireless personal area networks that are gaining significant traction within the new Internet-of-Things (IoT) paradigm. For multiple surveillance videos encoded with H.264/AVC and streamed via the 6tisch framework that simulates the IoT-oriented IEEE 802.15.4e TSCH medium access control, our solution is shown to be the only one that ensures all video bitstreams are delivered with acceptable quality in a deadlineabiding manner.Index Terms-wireless video sensor networks, resource allocation, non-stationary policies, IEEE 802.15.4e, Internet-of-Things

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Section: Review Of State-of-the-art In Video Streaming Over Wirelementioning

confidence: 99%

Section: A Benchmarksmentioning

confidence: 99%

Non-Stationary Resource Allocation Policies for Delay-Constrained Video Streaming: Application to Video over Internet-of-Things-Enabled Networks

Andrepoulos

Xiao

et al. 2014

IEEE J. Select. Areas Commun.

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“…The example shows an architecture consisting of a GW and a set of multicast groups MG i spread throughout the coverage. Multicast groups are defined as set of UTs which are under similar channel conditions and request the same type of video content [10]. This example could represent upcoming IPTV services such as Content Delivery Services (CDS) or Live Multimedia Broadcast (LMB).…”

Section: B Nc Based Optimizationmentioning

confidence: 99%

Offered capacity optimization mechanisms for multi-beam satellite systems

Alegre-Godoy

Alagha

Vázquez-Castro

2012

2012 IEEE International Conference on Communications (ICC)

Self Cite

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In this paper we investigate capacity optimization mechanisms for multi-beam satellite systems built on a realistic payload model. The first proposed mechanism deals with long term traffic variations, for which capacity optimization algorithms are proposed based on per-beam traffic requests. Due to the high asymmetry of the traffic, our algorithms provide time and spatial flexibility illuminating a specific set of beams within a window of several time-slots. Our algorithms maximize the amount of capacity actually offered while providing reduced power consumption. The second proposed mechanism deals with short-term traffic variations, for which we propose Network Coding (NC) based techniques at the link layer. The aim is to increase the offered capacity taking advantage of overlapping beam coverage, usually considered as a source of interference. This technique is meant to be applied not only in classical multibeam systems, but also on top of the per-beam capacity optimization as a method to deal with fast traffic unbalances not evaluated in the first mechanism. Analysis and simulations results show that system capacity can be increased up to 13% in the first case and up to 90% in the second case.

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“…Since the seminal work laid by Kelly [1], network utility maximization (NUM) has emerged as a promising framework in network congestion control protocol design and received wide investigation. Many literatures, for example, [2]- [8], have been published since. There are also books or tutorials on the theory of NUM; for example, [9]- [10].…”

Section: Introductionmentioning

confidence: 99%

“…To do so, we formulate our problem based on the links of a network because they manage how much resource (that is, bandwidth) each end user should obtain, just as in the distribution of wellbeing by governments. However, the fundamental difference of our work is that our algorithm is based on the optimization for each link rather than the global optimization of all users and all links together as done in other existing works [4], [6] and [8]. This allows for a distributed algorithm where each link along a data path can desire a source sending rate, which means that we can design our new scheme with the congestion signal fed back from the most congested link (which results in network-wise and maxmin fairness) instead of the cumulative congestion signal over the flow path.…”

Section: Introductionmentioning

confidence: 99%

OFEX Controller to Improve Queueing and User Performance in Multi-bottleneck Networks

Liu¹,

Yang²

2014

ETRI J

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We have designed and investigated a new congestion control scheme, called optimal and fully explicit (OFEX) controller. Different from existing relatively explicit controllers, this new scheme can provide not only optimal bandwidth allocation but also a fully explicit congestion signal to sources. It uses the congestion signal from the most congested link instead of the cumulative signal from the flow path. In this way, it overcomes the drawback of relatively explicit controllers exhibiting bias toward multi‐bottlenecked users and significantly improves their convergence speed and source throughput performance. Furthermore, our OFEX‐controller design considers a dynamic model by proposing a remedial measure against the unpredictable bandwidth changes in contention‐based multi‐access networks. Compared with former works/controllers, this remedy also effectively reduces the instantaneous queue size in a router and thus significantly improves queuing delay and packet loss performance. We have evaluated the effectiveness of the OFEX controller in OPNET. The experimental comparison with the existing relatively explicit controllers verifies the superiority of our new scheme.

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NUM-Based Fair Rate-Delay Balancing for Layered Video Multicasting over Adaptive Satellite Networks

Cited by 22 publications

References 23 publications

Non-Stationary Resource Allocation Policies for Delay-Constrained Video Streaming: Application to Video over Internet-of-Things-Enabled Networks

Non-Stationary Resource Allocation Policies for Delay-Constrained Video Streaming: Application to Video over Internet-of-Things-Enabled Networks

Offered capacity optimization mechanisms for multi-beam satellite systems

OFEX Controller to Improve Queueing and User Performance in Multi-bottleneck Networks

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