5,034 research outputs found
Congestion Control for Network-Aware Telehaptic Communication
Telehaptic applications involve delay-sensitive multimedia communication
between remote locations with distinct Quality of Service (QoS) requirements
for different media components. These QoS constraints pose a variety of
challenges, especially when the communication occurs over a shared network,
with unknown and time-varying cross-traffic. In this work, we propose a
transport layer congestion control protocol for telehaptic applications
operating over shared networks, termed as dynamic packetization module (DPM).
DPM is a lossless, network-aware protocol which tunes the telehaptic
packetization rate based on the level of congestion in the network. To monitor
the network congestion, we devise a novel network feedback module, which
communicates the end-to-end delays encountered by the telehaptic packets to the
respective transmitters with negligible overhead. Via extensive simulations, we
show that DPM meets the QoS requirements of telehaptic applications over a wide
range of network cross-traffic conditions. We also report qualitative results
of a real-time telepottery experiment with several human subjects, which reveal
that DPM preserves the quality of telehaptic activity even under heavily
congested network scenarios. Finally, we compare the performance of DPM with
several previously proposed telehaptic communication protocols and demonstrate
that DPM outperforms these protocols.Comment: 25 pages, 19 figure
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Error resilient video transcoding for robust inter-network communications using GPRS
A novel fully comprehensive mobile video communications
system is proposed in this paper. This system exploits
the useful rate management features of the video transcoders and
combines them with error resilience for transmissions of coded
video streams over general packet radio service (GPRS) mobileaccess
networks. The error-resilient video transcoding operation
takes place at a centralized point, referred to as a video proxy,
which provides the necessary output transmission rates with the
required amount of robustness. With the use of this proposed
algorithm, error resilience can be added to an already compressed
video stream at an intermediate stage at the edge of two or more
different networks through two resilience schemes, namely the
adaptive intra refresh (AIR) and feedback control signaling (FCS)
methods. Both resilience tools impose an output rate increase
which can also be prevented with the proposed novel technique in
this paper. Thus, an error-resilient video transcoding scheme is
presented to give robust video outputs at near target transmission
rates that only require the same number of GPRS timeslots as
the nonresilient schemes. Moreover, an ultimate robustness is
also accomplished with the combination of the two resilience
algorithms at the video proxy. Extensive computer simulations
demonstrate the effectiveness of the proposed system
Streaming Video QoE Modeling and Prediction: A Long Short-Term Memory Approach
HTTP based adaptive video streaming has become a popular choice of streaming
due to the reliable transmission and the flexibility offered to adapt to
varying network conditions. However, due to rate adaptation in adaptive
streaming, the quality of the videos at the client keeps varying with time
depending on the end-to-end network conditions. Further, varying network
conditions can lead to the video client running out of playback content
resulting in rebuffering events. These factors affect the user satisfaction and
cause degradation of the user quality of experience (QoE). It is important to
quantify the perceptual QoE of the streaming video users and monitor the same
in a continuous manner so that the QoE degradation can be minimized. However,
the continuous evaluation of QoE is challenging as it is determined by complex
dynamic interactions among the QoE influencing factors. Towards this end, we
present LSTM-QoE, a recurrent neural network based QoE prediction model using a
Long Short-Term Memory (LSTM) network. The LSTM-QoE is a network of cascaded
LSTM blocks to capture the nonlinearities and the complex temporal dependencies
involved in the time varying QoE. Based on an evaluation over several publicly
available continuous QoE databases, we demonstrate that the LSTM-QoE has the
capability to model the QoE dynamics effectively. We compare the proposed model
with the state-of-the-art QoE prediction models and show that it provides
superior performance across these databases. Further, we discuss the state
space perspective for the LSTM-QoE and show the efficacy of the state space
modeling approaches for QoE prediction
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