Correlation-aware packet scheduling for multi-camera streaming

Abstract

Abstract-In multi-view applications, multiple cameras acquire the same scene from different perspectives, which results in correlated video streams. It becomes important to exploit this correlation at the acquisition side (i.e., in the source coding) or at the receiver side (i.e., during error-concealment). In this work, we propose a correlation-aware scheduling algorithm for multi-camera sets, in which information from all views need to be sent over a bottleneck channel to clients that decode the 3D scene captured by the cameras. Based on a novel rate-distortion model, that takes into account the correlation between sources, we propose a solution that minimizes the distortion in the scene reconstruction and adapts to temporal variations in the scene content. Simulation results show the gain of the scheduling algorithm when the correlation model is known in the optimization, compared to scheduling policies with no information about the correlation or with a priori camera selection algorithms.

Correlation-Aware Packet Scheduling in Multi-Camera Networks

“…The overhead information required for this estimation thus corresponds to the information about the camera positions, which is generally of small size. As observed in [34], the exact value of the correlation level is however not a critical parameter in the scheduling optimization. Errors in the correlation evaluation, caused by a coarser estimation with a smaller overhead, does not have a significant impact on the scheduling policies.…”

Scheduling for Multi-Camera Surveillance in LTE Networks

“…Yang et al [11] proposed a cross-layer framework with QoSenabled streaming in a tele-immersive 3D multi-camera environment, which was later improved by considering dynamic bandwidth management in [12]. Moreover, multicamera surveillance has been explored in WiFi and sensor networks with limited resources [13], [14], [15]. Toni et al [13] considered the correlation between each packet and minimized the distortion in the scene reconstruction for multi-camera streaming in WiFi.…”

“…Moreover, multicamera surveillance has been explored in WiFi and sensor networks with limited resources [13], [14], [15]. Toni et al [13] considered the correlation between each packet and minimized the distortion in the scene reconstruction for multi-camera streaming in WiFi. Shiang and van der Schaar [14] focused on allocating the available time fraction to maximize received video quality while considering the distortion impact and delay constraints in multihop wireless networks.…”