Virtual reality (VR) imaging is 360°, which requires a large bandwidth for video transmission. To address this challenge, tile-based streaming has been proposed to deliver only the focused part of the video instead of the entire one. However, the impact of cybersickness, akin to motion sickness, on tile selection in VR has not been explored. In this paper, we investigate Multi-user Tile Streaming with Cybersickness Control (MTSCC) in an adaptive 360$^\circ$video streaming system with multicast and cybersickness alleviation. We propose a novel$m^{2}$-competitive online algorithm that utilizes Individual Sickness Indicator (ISI) and Bitrate Restriction Indicator (BRI) to evaluate user cybersickness tendency and network bandwidth efficiency. Moreover, we introduce the Video Loss Indicator (VLI) and Quality Variance Indicator (QVI) to assess video quality loss and quality difference between tiles. We also propose a multi-armed bandit (MAB) algorithm with confidence bound-based reward (video quality) and cost (cybersickness) estimation. The algorithm learns the weighting factor of each user's cost to slow down cybersickness accumulation for users with high cybersickness tendencies. We prove that the algorithm converges to an optimal solution over time. According to simulation with real network settings, our proposed algorithms outperform baselines in terms of video quality and cybersickness accumulation.

Virtual reality (VR) imaging, with its 360° perspective, poses bandwidth challenges for video transmission. To solve the challenge, tile-based streaming delivers only the viewed portion of the video rather than the whole video. However, the influence of VR sickness and social relations on tile quality assignment has not been fully investigated. In this paper, we formulate Tile and Handover Management regarding VR sickness and Social relation (THMVS) in an adaptive 360° video streaming system for mobile users, along with VR sickness alleviation. We propose a multi-armed bandit (MAB) algorithm with confidence bound-based reward and cost estimation to evaluate video quality and VR sickness. Our algorithm dynamically adjusts the tile quality and weighting factor of each user’s VR sickness increment according to user social relation, decelerating the VR sickness accumulation of susceptible individuals. Simulation results manifest that our algorithm outperforms existing video streaming algorithms regarding video quality and VR sickness accumulation.

Remote working and virtual socializing are becoming the norm. This trend evokes the growth of virtual reality (VR). Users can explore virtual environments (VEs) with the most intuitive approach: walking. In contrast to traditional static VR experience, redirected walking (RW) enables users to walk in VEs larger than the physical space. Recent RW works aim to deliver an obstacle-free VR experience. However, they ignore the user’s cybersickness and received signal strength, which degrades the VR experience. In this paper, we investigate Path Planning with Cybersickness and IRS Control (PPCIC) in an indoor RW system with cybersickness and IRS control. We proposed an algorithm with Cybersickness Indicator (CI) and SINR Indicator (SI) to evaluate the cybersickness and received signal strength of each path. Simulation results show that our algorithm outperforms the baselines regarding the total cost, cybersickness, and received signal strength.

In contrast to traditional videos, the imaging in virtual reality (VR) is 360°, and it consumes larger bandwidth to transmit video contents. To reduce bandwidth consumption, tile-based streaming has been proposed to deliver the focused part of the video, instead of the whole one. On the other hand, the techniques to alleviate cybersickness, which is akin to motion sickness and happens when using digital displays, have not been jointly explored with the tile selection in VR. In this paper, we investigate Tile Selection with Cybersickness Control (TSCC) in an adaptive 360° video streaming system with cybersickness alleviation. We propose an$m$-competitive online algorithm with Cybersickness Indicator (CI) and Video Loss Indicator (VLI) to evaluate instant cybersickness and the total loss of video quality. Moreover, the algorithm exploits Sickness Migration Indicator (SMI) to evaluate the cybersickness accumulated over time and the increase of optical flow to improve the tile quality assignment. Simulations with a real network dataset show that our algorithm outperforms the baselines regarding video quality and cybersickness accumulation.