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

Fine-Granularity Transmission Distortion Modeling for Video Packet Scheduling Over Mesh Networks

Digital Object Identifier 10.1109/TMM.2009.2036290Packet scheduling is a critical component in multi-session video streaming over mesh networks. Different video packets have different levels of contribution to the overall video presentation quality at the receiver side. In this work, we develop a fine-granularity transmission distortion model for the encoder to predict the quality degradation of decoded videos caused by lost video packets. Based on this packet-level transmission distortion model, we propose a content-and-deadline-aware scheduling (CDAS) scheme for multi-session video streaming over multi-hop mesh networks, where content priority, queuing delays, and dynamic network transmission conditions are jointly considered for each video packet. Our extensive experimental results demonstrate that the proposed transmission distortion model and the CDAS scheme significantly improve the performance of multi-session video streaming over mesh networks

Distortion Optimized Multipath Video Streaming

This paper addresses the problem of choosing the best streaming policy for distortion optimal multipath video delivery, under delay constraints. The streaming policy consists in a joint selection of the video packets to be transmitted, as well as their sending time, and the transmission path. A simple streaming model is introduced, which takes into account the video packet importance, and the dependencies among packets, and allows to compute the quality perceived by the receiver, as a function of the streaming policy. We derive an optimization problem based on the video abstraction model, under the assumption that the server knows the state of the network. A detailed analysis of the timing constraints in multipath video streaming provides helpful insights that lead to an efficient algorithm to solve the NP-hard policy optimization problem. We eventually propose a fast heuristic-based algorithm, that still provides close to optimal performance. Thanks to its limited complexity, this novel algorithm is finally implemented in live streaming scenarios, where it only induces a negligible distortion penalty compared to the optimal strategy. Simulation results finally show that the proposed scheduling solutions perform better than common scheduling algorithms, and represent very efficient strategies for both stored and live video streaming scenarios

Distributed Media Rate Allocation in Multipath Networks

The paper addresses the media-specific rate allocation problem in multipath networks. The streaming rate on each path is determined such that the end- to-end media distortion is minimized, when the receiving client aggregates packets received via multiple network channels. As it is difficult for the media server to have the full knowledge about the network status, we propose a distributed path selection and rate allocation algorithm. The network nodes participate to the optimization strategy, based on their local view of the network status. This eliminates the need for end-to-end network monitoring, and allows for the deployment of large scale rate allocation solutions. We design an optimal rate allocation algorithm, where the media client iteratively updates the best set of streaming paths. According to this rate allocation, each intermediate nodes then forwards incoming media flows on the outgoing paths, in a distributed manner. The proposed algorithm is shown to quickly converge to the optimal rate allocation solution, and hence to lead to stable rate allocation solutions. We also propose a greedy distributed algorithm that achieves close-to-optimal end-to-end distortion performance in a single pass. Both algorithms are shown to outperform simple heuristic- based rate allocation approaches for numerous random network topologies, and therefore offer an interesting solution for media-specific rate allocation over large scale multi-path networks

Packet Selection and Scheduling for Multipath Video Streaming

This paper addresses the problem of choosing the best streaming policy for distortion optimal multipath video delivery, under delay constraints. The streaming policy consists in a joint selection of the video packets to be transmitted, as well as their sending time, and the transmission path. A simple streaming model is introduced, which takes into account the video packet importance, and the dependencies among packets, and allows to compute the quality perceived by the receiver, as a function of the streaming policy. We derive an optimization problem based on the video abstraction model, under the assumption that the server knows, or can predict the state of the network. A detailed analysis of the timing constraints in multipath video streaming provides helpful insights that lead to an efficient algorithm to solve the NP-hard streaming policy optimization problem. We eventually propose a fast heuristic-based algorithm, that still provides close to optimal performance. Thanks to its limited complexity, this novel algorithm is finally demonstrated in live streaming scenarios, where it only induces a negligible distortion penalty compared to an optimal strategy. Simulation results finally show that the proposed scheduling solutions perform better than common scheduling algorithms, and represent very efficient multipath streaming strategies for both stored and live video services