5,247 research outputs found
A Survey on Cross-Layer Design Frameworks for Multimedia Applications over Wireless Networks
In the last few years, the Internet throughput, usage and reliability have
increased almost exponentially. The introduction of broadband wireless mobile
ad hoc networks (MANETs) and cellular networks together with increased
computational power have opened the door for a new breed of applications to be
created, namely real-time multimedia applications. Delivering real-time
multimedia traffic over a complex network like the Internet is a particularly
challenging task since these applications have strict quality -of-service (QoS)
requirements on bandwidth, delay, and delay jitter. Traditional IP-based best
effort service will not be able to meet these stringent requirements. The
time-varying nature of wireless channels and resource constrained wireless
devices make the problem even more difficult. To improve perceived media
quality by end users over wireless Internet, QoS supports can be addressed in
different layers, including application layer, transport layer and link layer.
Cross layer design is a well-known approach to achieve this adaptation. In
cross-layer design, the challenges from the physical wireless medium and the
QoS-demands from the applications are taken into account so that the rate,
power, and coding at the physical layer can adapted to meet the requirements of
the applications given the current channel and network conditions. A number of
propositions for cross-layer designs exist in the literature. In this paper, an
extensive review has been made on these cross-layer architectures that combine
the application-layer, transport layer and the link layer controls.
Particularly the issues like channel estimation techniques, adaptive controls
at the application and link layers for energy efficiency, priority based
scheduling, transmission rate control at the transport layer, and adaptive
automatic repeat request (ARQ) are discussed in detail.Comment: 16 pages, 9 figure
Delay-Constrained Video Transmission: Quality-driven Resource Allocation and Scheduling
Real-time video demands quality-of-service (QoS) guarantees such as delay
bounds for end-user satisfaction. Furthermore, the tolerable delay varies
depending on the use case such as live streaming or two-way video conferencing.
Due to the inherently stochastic nature of wireless fading channels,
deterministic delay bounds are difficult to guarantee. Instead, we propose
providing statistical delay guarantees using the concept of effective capacity.
We consider a multiuser setup whereby different users have (possibly different)
delay QoS constraints. We derive the resource allocation policy that maximizes
the sum video quality and applies to any quality metric with concave
rate-quality mapping. We show that the optimal operating point per user is such
that the rate-distortion slope is the inverse of the supported video source
rate per unit bandwidth, a key metric we refer to as the source spectral
efficiency. We also solve the alternative problem of fairness-based resource
allocation whereby the objective is to maximize the minimum video quality
across users. Finally, we derive user admission and scheduling policies that
enable selecting a maximal user subset such that all selected users can meet
their statistical delay requirement. Results show that video users with
differentiated QoS requirements can achieve similar video quality with vastly
different resource requirements. Thus, QoS-aware scheduling and resource
allocation enable supporting significantly more users under the same resource
constraints.Comment: Submitted to IEEE Journal of Selected Topics in Signal Processin
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
SVC-based Multi-user Streamloading for Wireless Networks
In this paper, we present an approach for joint rate allocation and quality
selection for a novel video streaming scheme called streamloading.
Streamloading is a recently developed method for delivering high quality video
without violating copyright enforced restrictions on content access for video
streaming. In regular streaming services, content providers restrict the amount
of viewable video that users can download prior to playback. This approach can
cause inferior user experience due to bandwidth variations, especially in
mobile networks with varying capacity. In streamloading, the video is encoded
using Scalable Video Coding, and users are allowed to pre-fetch enhancement
layers and store them on the device, while base layers are streamed in a near
real-time fashion ensuring that buffering constraints on viewable content are
met.
We begin by formulating the offline problem of jointly optimizing rate
allocation and quality selection for streamloading in a wireless network. This
motivates our proposed online algorithms for joint scheduling at the base
station and segment quality selection at receivers. The results indicate that
streamloading outperforms state-of-the-art streaming schemes in terms of the
number of additional streams we can admit for a given video quality.
Furthermore, the quality adaptation mechanism of our proposed algorithm
achieves a higher performance than baseline algorithms with no (or limited)
video-centric optimization of the base station's allocation of resources, e.g.,
proportional fairness
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
QoE-Aware Resource Allocation for Small Cells
In this paper, we study the problem of Quality of Experience (QoE) aware
resource allocation in wireless systems. In particular, we consider
application-aware joint Bandwidth-Power allocation for a small cell. We
optimize a QoE metric for multi-user video streaming in a small cell that
maintains a trade-off between maximizing the playback rate of each user and
ensuring proportional fairness (PF) among users. We formulate the
application-driven joint bandwidth-power allocation as a non-convex
optimization problem. However, we develop a polynomial complexity algorithm,
and we show that the proposed algorithm achieves the optimal solution of the
proposed optimization problem. Simulation results show that the proposed
QoE-aware algorithm significantly improves the average QoE. Moreover, it
outperforms the weighted sum rate allocation which is the state-of-the-art
physical resource allocation scheme.Comment: 6 page
Optimal Foresighted Multi-User Wireless Video
Recent years have seen an explosion in wireless video communication systems.
Optimization in such systems is crucial - but most existing methods intended to
optimize the performance of multi-user wireless video transmission are
inefficient. Some works (e.g. Network Utility Maximization (NUM)) are myopic:
they choose actions to maximize instantaneous video quality while ignoring the
future impact of these actions. Such myopic solutions are known to be inferior
to foresighted solutions that optimize the long-term video quality.
Alternatively, foresighted solutions such as rate-distortion optimized packet
scheduling focus on single-user wireless video transmission, while ignoring the
resource allocation among the users.
In this paper, we propose an optimal solution for performing joint
foresighted resource allocation and packet scheduling among multiple users
transmitting video over a shared wireless network. A key challenge in
developing foresighted solutions for multiple video users is that the users'
decisions are coupled. To decouple the users' decisions, we adopt a novel dual
decomposition approach, which differs from the conventional optimization
solutions such as NUM, and determines foresighted policies. Specifically, we
propose an informationally-decentralized algorithm in which the network manager
updates resource "prices" (i.e. the dual variables associated with the resource
constraints), and the users make individual video packet scheduling decisions
based on these prices. Because a priori knowledge of the system dynamics is
almost never available at run-time, the proposed solution can learn online,
concurrently with performing the foresighted optimization. Simulation results
show 7 dB and 3 dB improvements in Peak Signal-to-Noise Ratio (PSNR) over
myopic solutions and existing foresighted solutions, respectively
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
Performance Comparison of Packet Scheduling Algorithms for Video Traffic in LTE Cellular Network
In this paper we have studied downlink packet scheduling algorithms proposed
for LTE cellular networks. The study emphasize on three most promising
scheduling algorithms such as: FLS, EXP rule and LOG rule. The performance of
these three algorithms is conducted over video traffic in a vehicular
environment using LTE-Sim simulator. The simulation was setup with varying
number of users from 10 - 60 in fixed bounded regions of 1 km radius. The main
goal this study is to provide results that will help in the design process of
packet scheduler for LTE cellular networks, aiming to get better overall
performance users. Simulation results show that, the FLS scheme outperforms in
terms of average system throughput, average packet delay, PLR; and with a
satisfactory level of fairness index
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
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