117 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
Cross-layer optimization of a multimedia streaming system via dynamic programming
International audienceThis paper addresses the problem of efficient video streaming to mobile users. A cross-layer optimization of various parameters of the coding and transmission chain (coding parameters, buffer management, AC-layer management) is performed to account for the time-varying nature of the characteristics of the transmitted contents and of the wireless channel. The problem is cast in the framework of Markov Decision Processes (MDP). This formalism provides efficient tools to compute a foresighted control policy maximizing some long term discounted sum of rewards linked to the video quality received by the user. Experimental results illustrate the benefits in terms of average PSNR of this approach compared to a short-term (myopic) policy. The robustness of the proposed control policy to variations of the transmitted contents is also illustrated
Foresighted Demand Side Management
We consider a smart grid with an independent system operator (ISO), and
distributed aggregators who have energy storage and purchase energy from the
ISO to serve its customers. All the entities in the system are foresighted:
each aggregator seeks to minimize its own long-term payments for energy
purchase and operational costs of energy storage by deciding how much energy to
buy from the ISO, and the ISO seeks to minimize the long-term total cost of the
system (e.g. energy generation costs and the aggregators' costs) by dispatching
the energy production among the generators. The decision making of the entities
is complicated for two reasons. First, the information is decentralized: the
ISO does not know the aggregators' states (i.e. their energy consumption
requests from customers and the amount of energy in their storage), and each
aggregator does not know the other aggregators' states or the ISO's state (i.e.
the energy generation costs and the status of the transmission lines). Second,
the coupling among the aggregators is unknown to them. Specifically, each
aggregator's energy purchase affects the price, and hence the payments of the
other aggregators. However, none of them knows how its decision influences the
price because the price is determined by the ISO based on its state. We propose
a design framework in which the ISO provides each aggregator with a conjectured
future price, and each aggregator distributively minimizes its own long-term
cost based on its conjectured price as well as its local information. The
proposed framework can achieve the social optimum despite being decentralized
and involving complex coupling among the various entities
Adaptive Prioritized Random Linear Coding and Scheduling for Layered Data Delivery From Multiple Servers
In this paper, we deal with the problem of jointly determining the optimal coding strategy and the scheduling decisions when receivers obtain layered data from multiple servers. The layered data is encoded by means of prioritized random linear coding (PRLC) in order to be resilient to channel loss while respecting the unequal levels of importance in the data, and data blocks are transmitted simultaneously in order to reduce decoding delays and improve the delivery performance. We formulate the optimal coding and scheduling decisions problem in our novel framework with the help of Markov decision processes (MDP), which are effective tools for modeling adapting streaming systems. Reinforcement learning approaches are then proposed to derive reduced computational complexity solutions to the adaptive coding and scheduling problems. The novel reinforcement learning approaches and the MDP solution are examined in an illustrative example for scalable video transmission . Our methods offer large performance gains over competing methods that deliver the data blocks sequentially. The experimental evaluation also shows that our novel algorithms offer continuous playback and guarantee small quality variations which is not the case for baseline solutions. Finally, our work highlights the advantages of reinforcement learning algorithms to forecast the temporal evolution of data demands and to decide the optimal coding and scheduling decisions
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