630 research outputs found
Utility Optimal Scheduling and Admission Control for Adaptive Video Streaming in Small Cell Networks
We consider the jointly optimal design of a transmission scheduling and
admission control policy for adaptive video streaming over small cell networks.
We formulate the problem as a dynamic network utility maximization and observe
that it naturally decomposes into two subproblems: admission control and
transmission scheduling. The resulting algorithms are simple and suitable for
distributed implementation. The admission control decisions involve each user
choosing the quality of the video chunk asked for download, based on the
network congestion in its neighborhood. This form of admission control is
compatible with the current video streaming technology based on the DASH
protocol over TCP connections. Through simulations, we evaluate the performance
of the proposed algorithm under realistic assumptions for a small-cell network.Comment: 5 pages, 4 figures. Accepted and will be presented at IEEE
International Symposium on Information Theory (ISIT) 201
Analysis of Buffer Starvation with Application to Objective QoE Optimization of Streaming Services
Our purpose in this paper is to characterize buffer starvations for streaming
services. The buffer is modeled as an M/M/1 queue, plus the consideration of
bursty arrivals. When the buffer is empty, the service restarts after a certain
amount of packets are \emph{prefetched}. With this goal, we propose two
approaches to obtain the \emph{exact distribution} of the number of buffer
starvations, one of which is based on \emph{Ballot theorem}, and the other uses
recursive equations. The Ballot theorem approach gives an explicit result. We
extend this approach to the scenario with a constant playback rate using
T\`{a}kacs Ballot theorem. The recursive approach, though not offering an
explicit result, can obtain the distribution of starvations with
non-independent and identically distributed (i.i.d.) arrival process in which
an ON/OFF bursty arrival process is considered in this work. We further compute
the starvation probability as a function of the amount of prefetched packets
for a large number of files via a fluid analysis. Among many potential
applications of starvation analysis, we show how to apply it to optimize the
objective quality of experience (QoE) of media streaming, by exploiting the
tradeoff between startup/rebuffering delay and starvations.Comment: 9 pages, 7 figures; IEEE Infocom 201
Flow Level QoE of Video Streaming in Wireless Networks
The Quality of Experience (QoE) of streaming service is often degraded by
frequent playback interruptions. To mitigate the interruptions, the media
player prefetches streaming contents before starting playback, at a cost of
delay. We study the QoE of streaming from the perspective of flow dynamics.
First, a framework is developed for QoE when streaming users join the network
randomly and leave after downloading completion. We compute the distribution of
prefetching delay using partial differential equations (PDEs), and the
probability generating function of playout buffer starvations using ordinary
differential equations (ODEs) for CBR streaming. Second, we extend our
framework to characterize the throughput variation caused by opportunistic
scheduling at the base station, and the playback variation of VBR streaming.
Our study reveals that the flow dynamics is the fundamental reason of playback
starvation. The QoE of streaming service is dominated by the first moments such
as the average throughput of opportunistic scheduling and the mean playback
rate. While the variances of throughput and playback rate have very limited
impact on starvation behavior.Comment: 14 page
A Comprehensive Analysis of Swarming-based Live Streaming to Leverage Client Heterogeneity
Due to missing IP multicast support on an Internet scale, over-the-top media
streams are delivered with the help of overlays as used by content delivery
networks and their peer-to-peer (P2P) extensions. In this context,
mesh/pull-based swarming plays an important role either as pure streaming
approach or in combination with tree/push mechanisms. However, the impact of
realistic client populations with heterogeneous resources is not yet fully
understood. In this technical report, we contribute to closing this gap by
mathematically analysing the most basic scheduling mechanisms latest deadline
first (LDF) and earliest deadline first (EDF) in a continuous time Markov chain
framework and combining them into a simple, yet powerful, mixed strategy to
leverage inherent differences in client resources. The main contributions are
twofold: (1) a mathematical framework for swarming on random graphs is proposed
with a focus on LDF and EDF strategies in heterogeneous scenarios; (2) a mixed
strategy, named SchedMix, is proposed that leverages peer heterogeneity. The
proposed strategy, SchedMix is shown to outperform the other two strategies
using different abstractions: a mean-field theoretic analysis of buffer
probabilities, simulations of a stochastic model on random graphs, and a
full-stack implementation of a P2P streaming system.Comment: Technical report and supplementary material to
http://ieeexplore.ieee.org/document/7497234
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