562 research outputs found
Exploiting the Path Propagation Time Differences in Multipath Transmission with FEC
We consider a transmission of a delay-sensitive data stream from a single
source to a single destination. The reliability of this transmission may suffer
from bursty packet losses - the predominant type of failures in today's
Internet. An effective and well studied solution to this problem is to protect
the data by a Forward Error Correction (FEC) code and send the FEC packets over
multiple paths.
In this paper we show that the performance of such a multipath FEC scheme can
often be further improved. Our key observation is that the propagation times on
the available paths often significantly differ, typically by 10-100ms.
We propose to exploit these differences by appropriate packet scheduling that
we call `Spread'. We evaluate our solution with a precise, analytical
formulation and trace-driven simulations. Our studies show that Spread
substantially outperforms the state-of-the-art solutions. It typically achieves
two- to five-fold improvement (reduction) in the effective loss rate. Or
conversely, keeping the same level of effective loss rate, Spread significantly
decreases the observed delays and helps fighting the delay jitter.Comment: 12 page
Online multipath convolutional coding for real-time transmission
Most of multipath multimedia streaming proposals use Forward Error Correction
(FEC) approach to protect from packet losses. However, FEC does not sustain
well burst of losses even when packets from a given FEC block are spread over
multiple paths. In this article, we propose an online multipath convolutional
coding for real-time multipath streaming based on an on-the-fly coding scheme
called Tetrys. We evaluate the benefits brought out by this coding scheme
inside an existing FEC multipath load splitting proposal known as Encoded
Multipath Streaming (EMS). We demonstrate that Tetrys consistently outperforms
FEC in both uniform and burst losses with EMS scheme. We also propose a
modification of the standard EMS algorithm that greatly improves the
performance in terms of packet recovery. Finally, we analyze different
spreading policies of the Tetrys redundancy traffic between available paths and
observe that the longer propagation delay path should be preferably used to
carry repair packets.Comment: Online multipath convolutional coding for real-time transmission
(2012
Joint On-the-Fly Network Coding/Video Quality Adaptation for Real-Time Delivery
This paper introduces a redundancy adaptation algorithm for an on-the-fly
erasure network coding scheme called Tetrys in the context of real-time video
transmission. The algorithm exploits the relationship between the redundancy
ratio used by Tetrys and the gain or loss in encoding bit rate from changing a
video quality parameter called the Quantization Parameter (QP). Our evaluations
show that with equal or less bandwidth occupation, the video protected by
Tetrys with redundancy adaptation algorithm obtains a PSNR gain up to or more 4
dB compared to the video without Tetrys protection. We demonstrate that the
Tetrys redundancy adaptation algorithm performs well with the variations of
both loss pattern and delay induced by the networks. We also show that Tetrys
with the redundancy adaptation algorithm outperforms FEC with and without
redundancy adaptation
Exploiting the Path Propagation Time in Multipath Transmission with FEC
We consider a transmission of a delay-sensitive data stream from a single source to a single destination. The reliability of this transmission may suffer from bursty packet losses - the predominant type of failures in today's Internet. An effective and well studied solution to this problem is to protect the data by a Forward Error Correction (FEC) code and send the FEC packets over multiple paths. In this paper we show that the performance of such a multipath FEC scheme can often be further improved. Our key observation is that the propagation times on the available paths often significantly differ, usually by 10-100ms. We propose to exploit these differences by appropriate packet scheduling that we call `Spread'. We evaluate our solution with a precise, analytical formulation and trace-driven simulations. Our studies show that Spread substantially outperforms the state-of-the-art solutions. It typically achieves two- to five-fold improvement (reduction) in the effective loss rate. Or conversely, keeping the same level of effective loss rate, Spread significantly decreases the observed delays and helps fighting the delay jitter
AoA-aware Probabilistic Indoor Location Fingerprinting using Channel State Information
With expeditious development of wireless communications, location
fingerprinting (LF) has nurtured considerable indoor location based services
(ILBSs) in the field of Internet of Things (IoT). For most pattern-matching
based LF solutions, previous works either appeal to the simple received signal
strength (RSS), which suffers from dramatic performance degradation due to
sophisticated environmental dynamics, or rely on the fine-grained physical
layer channel state information (CSI), whose intricate structure leads to an
increased computational complexity. Meanwhile, the harsh indoor environment can
also breed similar radio signatures among certain predefined reference points
(RPs), which may be randomly distributed in the area of interest, thus mightily
tampering the location mapping accuracy. To work out these dilemmas, during the
offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI
amplitude as location fingerprint, which shares the structural simplicity of
RSS while reserving the most location-specific statistical channel information.
Moreover, an additional angle of arrival (AoA) fingerprint can be accurately
retrieved from CSI phase through an enhanced subspace based algorithm, which
serves to further eliminate the error-prone RP candidates. In the online phase,
by exploiting both CSI amplitude and phase information, a novel bivariate
kernel regression scheme is proposed to precisely infer the target's location.
Results from extensive indoor experiments validate the superior localization
performance of our proposed system over previous approaches
Analysis and simulation of feedback in network coded transmission
In questa tesi si propongono due protocolli di trasmissione multi-interfaccia, entrambi basati sul Network Coding, e studiamo un schema di feedback compatibile con questi e che permetta di sfruttare le proprietà di questo schema di codifica. Inoltre questi protocolli vengono implementati in un simulatore in linguaggio Python, e i risultati vengono ricavati tramite un'estensiva campagna di simulazioni, specialmente riguardo a overhead e feedbacks
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