1,671 research outputs found
Optimizing Quality of Experience of Dynamic Video Streaming over Fading Wireless Networks
We address the problem of video streaming packets from an Access Point (AP)
to multiple clients over a shared wireless channel with fading. In such
systems, each client maintains a buffer of packets from which to play the
video, and an outage occurs in the streaming whenever the buffer is empty.
Clients can switch to a lower-quality of video packet, or request packet
transmission at a higher energy level in order to minimize the number of
outages plus the number of outage periods and the number of low quality video
packets streamed, while there is an average power constraint on the AP.
We pose the problem of choosing the video quality and transmission power as a
Constrained Markov Decision Process (CMDP). We show that the problem involving
clients decomposes into MDPs, each involving only a single client, and
furthermore that the optimal policy has a threshold structure, in which the
decision to choose the video-quality and power-level of transmission depends
solely on the buffer-level
Wireless Video Caching and Dynamic Streaming under Differentiated Quality Requirements
This paper considers one-hop device-to-device (D2D)-assisted wireless caching
networks that cache video files of varying quality levels, with the assumption
that the base station can control the video quality but cache-enabled devices
cannot. Two problems arise in such a caching network: file placement problem
and node association problem. This paper suggests a method to cache videos of
different qualities, and thus of varying file sizes, by maximizing the sum of
video quality measures that users can enjoy. There exists an interesting
trade-off between video quality and video diversity, i.e., the ability to
provision diverse video files. By caching high-quality files, the cache-enabled
devices can provide high-quality video, but cannot cache a variety of files.
Conversely, when the device caches various files, it cannot provide a good
quality for file-requesting users. In addition, when multiple devices cache the
same file but their qualities are different, advanced node association is
required for file delivery. This paper proposes a node association algorithm
that maximizes time-averaged video quality for multiple users under a playback
delay constraint. In this algorithm, we also consider request collision, the
situation where several users request files from the same device at the same
time, and we propose two ways to cope with the collision: scheduling of one
user and non-orthogonal multiple access. Simulation results verify that the
proposed caching method and the node association algorithm work reliably.Comment: 13 pages, 11 figures, accepted for publication in IEEE Journal on
Selected Areas in Communication
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
Caching at the Wireless Edge: Design Aspects, Challenges and Future Directions
Caching at the wireless edge is a promising way of boosting spectral
efficiency and reducing energy consumption of wireless systems. These
improvements are rooted in the fact that popular contents are reused,
asynchronously, by many users. In this article, we first introduce methods to
predict the popularity distributions and user preferences, and the impact of
erroneous information. We then discuss the two aspects of caching systems,
namely content placement and delivery. We expound the key differences between
wired and wireless caching, and outline the differences in the system arising
from where the caching takes place, e.g., at base stations, or on the wireless
devices themselves. Special attention is paid to the essential limitations in
wireless caching, and possible tradeoffs between spectral efficiency, energy
efficiency and cache size.Comment: Published in IEEE Communications Magazin
A Control-Theoretic Approach to Adaptive Video Streaming in Dense Wireless Networks
Recently, the way people consume video content has been undergoing a dramatic
change. Plain TV sets, that have been the center of home entertainment for a
long time, are losing grounds to Hybrid TV's, PC's, game consoles, and, more
recently, mobile devices such as tablets and smartphones. The new predominant
paradigm is: watch what I want, when I want, and where I want.
The challenges of this shift are manifold. On the one hand, broadcast
technologies such as DVB-T/C/S need to be extended or replaced by mechanisms
supporting asynchronous viewing, such as IPTV and video streaming over
best-effort networks, while remaining scalable to millions of users. On the
other hand, the dramatic increase of wireless data traffic begins to stretch
the capabilities of the existing wireless infrastructure to its limits.
Finally, there is a challenge to video streaming technologies to cope with a
high heterogeneity of end-user devices and dynamically changing network
conditions, in particular in wireless and mobile networks.
In the present work, our goal is to design an efficient system that supports
a high number of unicast streaming sessions in a dense wireless access network.
We address this goal by jointly considering the two problems of wireless
transmission scheduling and video quality adaptation, using techniques inspired
by the robustness and simplicity of Proportional-Integral-Derivative (PID)
controllers. We show that the control-theoretic approach allows to efficiently
utilize available wireless resources, providing high Quality of Experience
(QoE) to a large number of users.Comment: Submitte
Enhancing User Experience for Multi-Screen Social TV Streaming over Wireless Networks
Recently, multi-screen cloud social TV is invented to transform TV into
social experience. People watching the same content on social TV may come from
different locations, while freely interact with each other through text, image,
audio and video. This crucial virtual living-room experience adds social
aspects into existing performance metrics. In this paper, we parse social TV
user experience into three elements (i.e., inter-user delay, video quality of
experience (QoE), and resource efficiency), and provide a joint analytical
framework to enhance user experience. Specifically, we propose a cloud-based
optimal playback rate allocation scheme to maximize the overall QoE while upper
bounding inter-user delay. Experiment results show that our algorithm achieves
near-optimal tradeoff between inter-user delay and video quality, and
demonstrates resilient performance even under very fast wireless channel
fading.Comment: submitted to IEEE GLOBECOM 201
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
Exploiting Network Awareness to Enhance DASH Over Wireless
The introduction of Dynamic Adaptive Streaming over HTTP (DASH) helped reduce
the consumption of resource in video delivery, but its client-based rate
adaptation is unable to optimally use the available end-to-end network
bandwidth. We consider the problem of optimizing the delivery of video content
to mobile clients while meeting the constraints imposed by the available
network resources. Observing the bandwidth available in the network's two main
components, core network, transferring the video from the servers to edge nodes
close to the client, and the edge network, which is in charge of transferring
the content to the user, via wireless links, we aim to find an optimal solution
by exploiting the predictability of future user requests of sequential video
segments, as well as the knowledge of available infrastructural resources at
the core and edge wireless networks in a given future time window. Instead of
regarding the bottleneck of the end-to-end connection as our throughput, we
distribute the traffic load over time and use intermediate nodes between the
server and the client for buffering video content to achieve higher throughput,
and ultimately significantly improve the Quality of Experience for the end user
in comparison with current solutions
WiFlix: Adaptive Video Streaming in Massive MU-MIMO Wireless Networks
We consider the problem of simultaneous on-demand streaming of stored video
to multiple users in a multi-cell wireless network where multiple unicast
streaming sessions are run in parallel and share the same frequency band. Each
streaming session is formed by the sequential transmission of video "chunks,"
such that each chunk arrives into the corresponding user playback buffer within
its playback deadline. We formulate the problem as a Network Utility
Maximization (NUM) where the objective is to fairly maximize users' video
streaming Quality of Experience (QoE) and then derive an iterative control
policy using Lyapunov Optimization, which solves the NUM problem up to any
level of accuracy and yields an online protocol with control actions at every
iteration decomposing into two layers interconnected by the users' request
queues : i) a video streaming adaptation layer reminiscent of DASH, implemented
at each user node; ii) a transmission scheduling layer where a max-weight
scheduler is implemented at each base station. The proposed chunk request
scheme is a pull strategy where every user opportunistically requests video
chunks from the neighboring base stations and dynamically adapts the quality of
its requests based on the current size of the request queue. For the
transmission scheduling component, we first describe the general max-weight
scheduler and then particularize it to a wireless network where the base
stations have multiuser MIMO (MU-MIMO) beamforming capabilities. We exploit the
channel hardening effect of large-dimensional MIMO channels (massive MIMO) and
devise a low complexity user selection scheme to solve the underlying
combinatorial problem of selecting user subsets for downlink beamforming, which
can be easily implemented and run independently at each base station.Comment: 30 pages. arXiv admin note: text overlap with arXiv:1304.808
Anticipatory Radio Resource Management for Mobile Video Streaming with Linear Programming
In anticipatory networking, channel prediction is used to improve
communication performance. This paper describes a new approach for allocating
resources to video streaming traffic while accounting for quality of service.
The proposed method is based on integrating a model of the user's local
play-out buffer into the radio access network. The linearity of this model
allows to formulate a Linear Programming problem that optimizes the trade-off
between the allocated resources and the stalling time of the media stream. Our
simulation results demonstrate the full power of anticipatory optimization in a
simple, yet representative, scenario. Compared to instantaneous adaptation, our
anticipatory solution shows impressive gains in spectral efficiency and
stalling duration at feasible computation time while being robust against
prediction errors.Comment: 6 pages, 5 figures, ICC201
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