513 research outputs found
A Deep Learning Approach for Low-Latency Packet Loss Concealment of Audio Signals in Networked Music Performance Applications
Networked Music Performance (NMP) is envisioned as a potential game changer
among Internet applications: it aims at revolutionizing the traditional concept
of musical interaction by enabling remote musicians to interact and perform
together through a telecommunication network. Ensuring realistic conditions for
music performance, however, constitutes a significant engineering challenge due
to extremely strict requirements in terms of audio quality and, most
importantly, network delay. To minimize the end-to-end delay experienced by the
musicians, typical implementations of NMP applications use un-compressed,
bidirectional audio streams and leverage UDP as transport protocol. Being
connection less and unreliable,audio packets transmitted via UDP which become
lost in transit are not re-transmitted and thus cause glitches in the receiver
audio playout. This article describes a technique for predicting lost packet
content in real-time using a deep learning approach. The ability of concealing
errors in real time can help mitigate audio impairments caused by packet
losses, thus improving the quality of audio playout in real-world scenarios.Comment: 8 pages, 2 figure
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
Enhancement of Adaptive Forward Error Correction Mechanism for Video Transmission Over Wireless Local Area Network
Video transmission over the wireless network faces many challenges. The most critical challenge is related to packet loss. To overcome the problem of packet loss,
Forward Error Correction is used by adding extra packets known as redundant packet or parity packet. Currently, FEC mechanisms have been adopted together with Automatic Repeat reQuest (ARQ) mechanism to overcome packet losses and avoid network congestion in various wireless network conditions. The number of FEC packets need to be generated effectively because wireless network usually has varying network conditions. In the current Adaptive FEC mechanism, the FEC packets are decided by the average queue length and average packet retransmission times. The Adaptive FEC mechanisms have been proposed to suit the network condition by generating FEC packets adaptively in the wireless network. However, the current Adaptive FEC mechanism has some major drawbacks such as the reduction of recovery performance which injects too many excessive FEC packets into the network. This is not flexible enough to adapt with varying wireless network condition. Therefore, the enhancement of Adaptive FEC mechanism (AFEC) known as Enhanced Adaptive FEC (EnAFEC) has been proposed. The aim is to improve recovery performance on the current Adaptive FEC mechanism by injecting FEC packets dynamically based on varying wireless network conditions. The EnAFEC mechanism is implemented in the simulation environment using Network Simulator 2 (NS-2). Performance evaluations are also carried out. The EnAFEC was tested with the random uniform error model. The results from experiments and performance analyses showed that EnAFEC mechanism outperformed the other Adaptive FEC mechanism in terms of recovery efficiency. Based on the findings, the optimal amount of FEC generated by EnAFEC mechanism can recover high packet loss and produce good video quality
Adaptive header compression techniques for mobile multimedia networks
EThOS - Electronic Theses Online ServiceGBUnited Kingdo
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