1,390 research outputs found
A Survey on QoE-oriented Wireless Resources Scheduling
Future wireless systems are expected to provide a wide range of services to
more and more users. Advanced scheduling strategies thus arise not only to
perform efficient radio resource management, but also to provide fairness among
the users. On the other hand, the users' perceived quality, i.e., Quality of
Experience (QoE), is becoming one of the main drivers within the schedulers
design. In this context, this paper starts by providing a comprehension of what
is QoE and an overview of the evolution of wireless scheduling techniques.
Afterwards, a survey on the most recent QoE-based scheduling strategies for
wireless systems is presented, highlighting the application/service of the
different approaches reported in the literature, as well as the parameters that
were taken into account for QoE optimization. Therefore, this paper aims at
helping readers interested in learning the basic concepts of QoE-oriented
wireless resources scheduling, as well as getting in touch with its current
research frontier.Comment: Revised version: updated according to the most recent related
literature; added references; corrected typo
Predictive Green Wireless Access: Exploiting Mobility and Application Information
The ever increasing mobile data traffic and dense deployment of wireless
networks have made energy efficient radio access imperative. As networks are
designed to satisfy peak user demands, radio access energy can be reduced in a
number of ways at times of lower demand. This includes putting base stations
(BSs) to intermittent short sleep modes during low load, as well as adaptively
powering down select BSs completely where demand is low for prolonged time
periods. In order to fully exploit such energy conserving mechanisms, networks
should be aware of the user temporal and spatial traffic demands. To this end,
this article investigates the potential of utilizing predictions of user
location and application information as a means to energy saving. We discuss
the development of a predictive green wireless access (PreGWA) framework and
identify its key functional entities and their interaction. To demonstrate the
potential energy savings we then provide a case study on stored video streaming
and illustrate how exploiting predictions can minimize BS resource consumption
within a single cell, and across a network of cells. Finally, to emphasize the
practical potential of PreGWA, we present a distributed heuristic that reduces
resource consumption significantly without requiring considerable information
or signaling overhead
Effective Capacity in Wireless Networks: A Comprehensive Survey
Low latency applications, such as multimedia communications, autonomous
vehicles, and Tactile Internet are the emerging applications for
next-generation wireless networks, such as 5th generation (5G) mobile networks.
Existing physical-layer channel models, however, do not explicitly consider
quality-of-service (QoS) aware related parameters under specific delay
constraints. To investigate the performance of low-latency applications in
future networks, a new mathematical framework is needed. Effective capacity
(EC), which is a link-layer channel model with QoS-awareness, can be used to
investigate the performance of wireless networks under certain statistical
delay constraints. In this paper, we provide a comprehensive survey on existing
works, that use the EC model in various wireless networks. We summarize the
work related to EC for different networks such as cognitive radio networks
(CRNs), cellular networks, relay networks, adhoc networks, and mesh networks.
We explore five case studies encompassing EC operation with different design
and architectural requirements. We survey various delay-sensitive applications
such as voice and video with their EC analysis under certain delay constraints.
We finally present the future research directions with open issues covering EC
maximization
Aqua Computing: Coupling Computing and Communications
The authors introduce a new vision for providing computing services for
connected devices. It is based on the key concept that future computing
resources will be coupled with communication resources, for enhancing user
experience of the connected users, and also for optimising resources in the
providers' infrastructures. Such coupling is achieved by Joint/Cooperative
resource allocation algorithms, by integrating computing and communication
services and by integrating hardware in networks. Such type of computing, by
which computing services are not delivered independently but dependent of
networking services, is named Aqua Computing. The authors see Aqua Computing as
a novel approach for delivering computing resources to end devices, where
computing power of the devices are enhanced automatically once they are
connected to an Aqua Computing enabled network. The process of resource
coupling is named computation dissolving. Then, an Aqua Computing architecture
is proposed for mobile edge networks, in which computing and wireless
networking resources are allocated jointly or cooperatively by a Mobile Cloud
Controller, for the benefit of the end-users and/or for the benefit of the
service providers. Finally, a working prototype of the system is shown and the
gathered results show the performance of the Aqua Computing prototype.Comment: A shorter version of this paper will be submitted to an IEEE magazin
A Lookback Scheduling Framework for Long-Term Quality-of-Service Over Multiple Cells
In current cellular networks, schedulers allocate wireless channel resources
to users based on instantaneous channel gains and short-term moving averages of
user rates and queue lengths. By using only such short-term information,
schedulers ignore the users' service history in previous cells and, thus,
cannot guarantee long-term Quality of Service (QoS) when users traverse
multiple cells with varying load and capacity. In this paper, we propose a new
Long-term Lookback Scheduling (LLS) framework, which extends conventional
short-term scheduling with long-term QoS information from previously traversed
cells. We demonstrate the application of LLS for common channel-aware, as well
as channel and queue-aware schedulers. The developed long-term schedulers also
provide a controllable trade-off between emphasizing the immediate user QoS or
the long-term measures. Our simulation results show high gains in long-term QoS
without sacrificing short-term user requirements. Therefore, the proposed
scheduling approach improves subscriber satisfaction and increases operational
efficiency
Greedy-Knapsack Algorithm for Optimal Downlink Resource Allocation in LTE Networks
The Long Term Evolution (LTE) as a mobile broadband technology supports a
wide domain of communication services with different requirements. Therefore,
scheduling of all flows from various applications in overload states in which
the requested amount of bandwidth exceeds the limited available spectrum
resources is a challenging issue. Accordingly, in this paper, a greedy
algorithm is presented to evaluate user candidates which are waiting for
scheduling and select an optimal set of the users to maximize system
performance, without exceeding available bandwidth capacity. The
greedy-knapsack algorithm is defined as an optimal solution to the resource
allocation problem, formulated based on the fractional knapsack problem. A
compromise between throughput and QoS provisioning is obtained by proposing a
class-based ranking function, which is a combination of throughput and QoS
related parameters defined for each application. The simulation results show
that the proposed method provides high performance in terms of throughput, loss
and delay for different classes of QoS over the existing ones, especially under
overload traffic.Comment: Wireless Networks, 201
Toward Green Media Delivery: Location-Aware Opportunities and Approaches
Mobile media has undoubtedly become the predominant source of traffic in
wireless networks. The result is not only congestion and poor
Quality-of-Experience, but also an unprecedented energy drain at both the
network and user devices. In order to sustain this continued growth, novel
disruptive paradigms of media delivery are urgently needed. We envision that
two key contemporary advancements can be leveraged to develop greener media
delivery platforms: 1) the proliferation of navigation hardware and software in
mobile devices has created an era of location-awareness, where both the current
and future user locations can be predicted; and 2) the rise of context-aware
network architectures and self-organizing functionalities is enabling context
signaling and in-network adaptation. With these developments in mind, this
article investigates the opportunities of exploiting location-awareness to
enable green end-to-end media delivery. In particular, we discuss and propose
approaches for location-based adaptive video quality planning, in-network
caching, content prefetching, and long-term radio resource management. To
provide insights on the energy savings, we then present a cross-layer framework
that jointly optimizes resource allocation and multi-user video quality using
location predictions. Finally, we highlight some of the future research
directions for location-aware media delivery in the conclusion
Energy-Efficient Adaptive Video Transmission: Exploiting Rate Predictions in Wireless Networks
The unprecedented growth of mobile video traffic is adding significant
pressure to the energy drain at both the network and the end user. Energy
efficient video transmission techniques are thus imperative to cope with the
challenge of satisfying user demand at sustainable costs. In this paper, we
investigate how predicted user rates can be exploited for energy efficient
video streaming with the popular HTTP-based Adaptive Streaming (AS) protocols
(e.g. DASH). To this end, we develop an energy-efficient Predictive Green
Streaming (PGS) optimization framework that leverages predictions of wireless
data rates to achieve the following objectives 1) minimize the required
transmission airtime without causing streaming interruptions, 2) minimize total
downlink Base Station (BS) power consumption for cases where BSs can be
switched off in deep sleep, and 3) enable a trade-off between AS quality and
energy consumption. Our framework is first formulated as a Mixed Integer Linear
Program (MILP) where decisions on multi-user rate allocation, video segment
quality, and BS transmit power are jointly optimized. Then, to provide an
online solution, we present a polynomial-time heuristic algorithm that
decouples the PGS problem into multiple stages. We provide a performance
analysis of the proposed methods by simulations, and numerical results
demonstrate that the PGS framework yields significant energy savings.Comment: 14 pages, 14 figures, accepted for publication in IEEE Transactions
on Vehicular Technolog
Scalable Application- and User-aware Resource Allocation in Enterprise Networks Using End-host Pacing
Scalable user- and application-aware resource allocation for heterogeneous
applications sharing an enterprise network is still an unresolved problem. The
main challenges are: (i) How to define user- and application-aware shares of
resources? (ii) How to determine an allocation of shares of network resources
to applications? (iii) How to allocate the shares per application in
heterogeneous networks at scale? In this paper we propose solutions to the
three challenges and introduce a system design for enterprise deployment.
Defining the necessary resource shares per application is hard, as the intended
use case and user's preferences influence the resource demand. Utility
functions based on user experience enable a mapping of network resources in
terms of throughput and latency budget to a common user-level utility scale. A
multi-objective MILP is formulated to solve the throughput- and delay-aware
embedding of each utility function for a max-min fairness criteria. The
allocation of resources in traditional networks with policing and scheduling
cannot distinguish large numbers of classes. We propose a resource allocation
system design for enterprise networks based on Software-Defined Networking
principles to achieve delay-constrained routing in the network and application
pacing at the end-hosts. The system design is evaluated against best effort
networks with applications competing for the throughput of a constrained link.
The competing applications belong to the five application classes web browsing,
file download, remote terminal work, video streaming, and Voice-over-IP. The
results show that the proposed methodology improves the minimum and total
utility, minimizes packet loss and queuing delay at bottlenecks, establishes
fairness in terms of utility between applications, and achieves predictable
application performance at high link utilization.Comment: Accepted for publication in ACM Transactions on Modeling and
Performance Evaluation of Computing Systems (TOMPECS
Optimal Network-Assisted Multi-user DASH Video Streaming
Streaming video is becoming the predominant type of traffic over the Internet
with reports forecasting the video content to account for 80% of all traffic by
2019. With significant investment on Internet backbone, the main bottleneck
remains at the edge servers (e.g., WiFi access points, small cells, etc.). In
this work, we propose and prove the optimality of a multiuser resource
allocation mechanism operating at the edge server that minimizes the
probability of stalling of video streams due to buffer under-flows. Our
proposed policy utilizes Media Presentation Description (MPD) files of clients
that are sent in compliant to Dynamic Adaptive Streaming over HTTP (DASH)
protocol to be cognizant of the deadlines of each of the media file to be
displayed by the clients. Then, the policy schedules the users in the order of
their deadlines. After establishing the optimality of this policy to minimize
the stalling probability for a network with links associated with fixed loss
rates, the utility of the algorithm is verified under realistic network
conditions with detailed NS-3 simulations
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