Joint transmission scheduling and congestion control for adaptive streaming in wireless device-to-device networks

Bethanabhotla, Dilip; Caire, Giuseppe; Neely, Michael J.

doi:10.1109/acssc.2012.6489207

Cited by 20 publications

(45 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…k ∀ ∈K Notice that constraint (9b) corresponds to stability of the virtual queues given in equation (8), since k γ and k R are the time-averaged arrival rate and the time-averaged service rate for the virtual queue W k (t), respectively. From Bethanabhotla et al (2013), we can find that the optimal utility value is the same for both problems P1 and P2.…”

Section: Dynamic Resource Allocation Schemesmentioning

confidence: 80%

Dynamic resource allocation for multimedia services delivery in cellular/infostation integrated HSR networks

Yan

Zhu

et al. 2017

IJAHUC

View full text Add to dashboard Cite

Abstract:With the rapid development of high-speed railway (HSR) system, the resource allocation problem in HSR wireless network becomes one of the key issues to improve the efficiency of resource utilisation. In this paper, we investigate the downlink resource allocation problem for multimedia services delivery in HSR multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) system with a cellular/infostation integrated network architecture. Taking the train trajectory and network stability into account, we formulate the problem as a stochastic network optimisation programming, which aims at maximising the overall system utility while keeping the system stable under the total transmission power constraint. To address the NP-hard mixed integer programming, the original problem is firstly transformed into a queue stability problem, and then decomposed into two separate subproblems by the drift-plus-penalty (DPP) approach. Finally, based on the stochastic optimisation technique, a dynamic resource allocation algorithm is proposed and its efficiency is illustrated by theoretical analysis and numerical simulations.

show abstract

Section: Dynamic Resource Allocation Schemesmentioning

confidence: 80%

Dynamic resource allocation for multimedia services delivery in cellular/infostation integrated HSR networks

Yan

Zhu

et al. 2017

IJAHUC

View full text Add to dashboard Cite

show abstract

“…Invoking the rate constraints (3) and observing that 4 Further, integrating the capacity constraints (4) over t ∈ [0, T ], we obtain s∈S r∈Rs…”

Section: Proposition 1 Opt 0 (T) Is Bounded From Above Sample Path Wmentioning

confidence: 99%

“…Somewhat related problems have been considered in several recent papers, see for instance [4], [13], [16], [17], [19], [25]. While these papers account for deadlines associated with real-time streaming ( [13], [17], [25]), general video quality models ( [19]), general network settings ( [4]) etc, the impact of user dynamics is ignored.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal rate allocation for adaptive wireless video streaming in networks with user dynamics

Joseph

Borst²,

Reiman³

2014

IEEE INFOCOM 2014 - IEEE Conference on Computer Communications

View full text Add to dashboard Cite

We consider the problem of optimal rate allocation and admission control for adaptive video streaming sessions in wireless networks with user dynamics. The central aim is to achieve an optimal tradeoff between several key objectives: maximizing the average rate utility per user, minimizing the temporal rate variability, and maximizing the number of users supported. We identify the structure of algorithms that achieve asymptotically optimal performance in large-capacity systems, and exploit the insight into this structure to devise parsimonious and robust online algorithms. Extensive simulation experiments demonstrate that the proposed online algorithms perform well, even in systems with relatively small capacity.

show abstract

“…In [17], the authors propose a real time pricing algorithm for smart grids based on the optimization of users' aggregated utility by selecting each user's energy consumption schedule, under constraints of energy availability. Yet another example can be found in [6], where a NUM problem is used for joint transmission scheduling and congestion control for adaptive streaming in the context of wireless Device-to-Device networks.…”

Section: Introductionmentioning

confidence: 99%

Revenue sharing in network utility maximization problems

2016

View full text Add to dashboard Cite

Alliances arise in a wide variety of domains, when a group of countries, political parties, people or other entities agree to work together because of shared interests or aims. They make sense, if the output obtained is somehow better than the outcome of acting individually. Revenue or cost sharing is key when determining if individuals are better off by contributing to an alliance or not. In our alliance each member owns a unique resource -or set of resources-, which is given to the alliance . The alliance sells services, which are supported thanks to one or a set of these resources. We focus on alliances that sell services in such a way that the total revenue of the alliance is maximized. We show that this kind of problems can be modeled through a Network Utility Maximization problem. We subsequently explore the problem of revenue sharing among the members of the alliance. Such a problem is a complex one since the interests of all participants must be ensured and correct incentives must be provided. We formally formulate the members' interests through a set of properties the revenue sharing method should verify. We then discuss the existing methods for revenue sharing and conclude that none of them verify the needed properties for the case of a revenue maximizing alliance. We finally propose a revenue sharing method based on projecting the contributions of each member of the alliance into an economic stable set. Through an exhaustive simulative study we conclude that our method provides, in addition to economic stabil-I. Amigo, S.Vaton Institut Mines Telecom, Télécom Bretagne Technopole Brest-Iroise -CS 83818 29238 Brest Cedex 3, France Phone: (+33)(0)2 29 00 11 11 E-mail: {isabel.amigo,sandrine.vaton}@telecom-bretagne.eu P. Belzarena Facultad de Ingeniería, Universidad de la República Julio Herrera y Reissig 565 -C.P. 11300 Montevideo, Uruguay Phone: (+598) 2711 0974 E-mail: {belza,iamigo}@fing.edu.uy 2 I. Amigo, P. Belzarena, S. Vaton ity, fairness among members and the right incentives to them. Through our analysis Network Service Provider alliances, which sell quality-assured data transport services, are considered as an application example.

show abstract

Joint transmission scheduling and congestion control for adaptive streaming in wireless device-to-device networks

Cited by 20 publications

References 14 publications

Dynamic resource allocation for multimedia services delivery in cellular/infostation integrated HSR networks

Dynamic resource allocation for multimedia services delivery in cellular/infostation integrated HSR networks

Optimal rate allocation for adaptive wireless video streaming in networks with user dynamics

Revenue sharing in network utility maximization problems

Contact Info

Product

Resources

About