4,369 research outputs found
Multi-View Video Packet Scheduling
In multiview applications, multiple cameras acquire the same scene from
different viewpoints and generally produce correlated video streams. This
results in large amounts of highly redundant data. In order to save resources,
it is critical to handle properly this correlation during encoding and
transmission of the multiview data. In this work, we propose a
correlation-aware packet scheduling algorithm for multi-camera networks, where
information from all cameras are transmitted over a bottleneck channel to
clients that reconstruct the multiview images. The scheduling algorithm relies
on a new rate-distortion model that captures the importance of each view in the
scene reconstruction. We propose a problem formulation for the optimization of
the packet scheduling policies, which adapt to variations in the scene content.
Then, we design a low complexity scheduling algorithm based on a trellis search
that selects the subset of candidate packets to be transmitted towards
effective multiview reconstruction at clients. Extensive simulation results
confirm the gain of our scheduling algorithm when inter-source correlation
information is used in the scheduler, compared to scheduling policies with no
information about the correlation or non-adaptive scheduling policies. We
finally show that increasing the optimization horizon in the packet scheduling
algorithm improves the transmission performance, especially in scenarios where
the level of correlation rapidly varies with time
Bandwidth efficient multi-station wireless streaming based on complete complementary sequences
Data streaming from multiple base stations to a client is recognized as a robust technique for multimedia streaming. However the resulting transmission in parallel over wireless channels poses serious challenges, especially multiple access interference, multipath fading, noise effects and synchronization. Spread spectrum techniques seem the obvious choice to mitigate these effects, but at the cost of increased bandwidth requirements. This paper proposes a solution that exploits complete complementary spectrum spreading and data compression techniques jointly to resolve the communication challenges whilst ensuring efficient use of spectrum and acceptable bit error rate. Our proposed spreading scheme reduces the required transmission bandwidth by exploiting correlation among information present at multiple base stations. Results obtained show 1.75 Mchip/sec (or 25%) reduction in transmission rate, with only up to 6 dB loss in frequency-selective channel compared to a straightforward solution based solely on complete complementary spectrum spreading
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
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