3,034 research outputs found
Distributed Rate Allocation Policies for Multi-Homed Video Streaming over Heterogeneous Access Networks
We consider the problem of rate allocation among multiple simultaneous video
streams sharing multiple heterogeneous access networks. We develop and evaluate
an analytical framework for optimal rate allocation based on observed available
bit rate (ABR) and round-trip time (RTT) over each access network and video
distortion-rate (DR) characteristics. The rate allocation is formulated as a
convex optimization problem that minimizes the total expected distortion of all
video streams. We present a distributed approximation of its solution and
compare its performance against H-infinity optimal control and two heuristic
schemes based on TCP-style additive-increase-multiplicative decrease (AIMD)
principles. The various rate allocation schemes are evaluated in simulations of
multiple high-definition (HD) video streams sharing multiple access networks.
Our results demonstrate that, in comparison with heuristic AIMD-based schemes,
both media-aware allocation and H-infinity optimal control benefit from
proactive congestion avoidance and reduce the average packet loss rate from 45%
to below 2%. Improvement in average received video quality ranges between 1.5
to 10.7 dB in PSNR for various background traffic loads and video playout
deadlines. Media-aware allocation further exploits its knowledge of the video
DR characteristics to achieve a more balanced video quality among all streams.Comment: 12 pages, 22 figure
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
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