Abstract
Cloud Virtual Reality (VR) technology is expected to promote VR by providing a higher Quality of Experience (QoE) and energy efficiency at lower prices for the consumer. In cloud VR, the virtual environment is rendered on the remote server and transmitted to the headset as a video stream. To guarantee real-time experience, networks need to transfer huge amounts of data with much stricter delays than imposed by the state-of-the-art live video streaming applications. To reduce the burden imposed on the networks, cloud VR applications shall adequately react to the changing network conditions, including the wireless channel fluctuations and highly variable user activity. For that, they need to adjust the quality of the video stream adaptively. This paper studies video quality adaptation for cloud VR and improves the QoE for cloud VR users. It develops a distributed, i.e., with no assistance from the network, bitrate adaptation algorithm for cloud VR, called the Enhanced VR bitrate Estimator (EVeREst). The algorithm aims to optimize the average bitrate of cloud VR video flows subject to video frame delay and loss constraints. For that, the algorithm estimates both the current network load and the delay experienced by separate frames. It anticipates the changes in the users’ activity and limits the bitrate accordingly, which helps prevent excess interruptions of the playback. With simulations, the paper shows that the developed algorithm significantly improves the QoE for the end-users compared to the state-of-the-art adaptation algorithms developed for MPEG DASH live streaming, e.g., BOLA. Unlike these algorithms, the developed algorithm satisfies the frame loss requirements of multiple VR sessions and increases the network goodput by up to 10 times.
Highlights
Numerous Virtual Reality (VR) applications have emerged recently to improve entertainment [1,2], medicine [3,4], engineering [5,6], and other spheres of everyday life.To provide an immersive experience, such applications require minimal feedback delay and high image quality
The server renders the environment according to the received data and sends it back to the Head-Mounted Display (HMD) as a video stream
To estimate its influence on the Quality of Experience (QoE), we introduce the concept of a VR session satisfied with the frame loss ratio, i.e., the session that has lost less than θ% of the frames
Summary
Numerous Virtual Reality (VR) applications have emerged recently to improve entertainment [1,2], medicine [3,4], engineering [5,6], and other spheres of everyday life.To provide an immersive experience, such applications require minimal feedback delay and high image quality. In cloud VR, the headset, called the Head-Mounted Display (HMD), only monitors the user’s actions and sends the data to the remote server. The server renders the environment according to the received data and sends it back to the HMD as a video stream. Many papers address the problem of video bitrate adaptation design The majority of such papers propose the algorithms for MPEG DASH-based video-on-demand streaming. Apart from that, some papers, e.g., [51,52,53], study frameworks that assume centralized network-assisted bitrate adaptation. Such an approach simplifies the problem because intermediate network nodes have more information about the network state. The approach is very efficient, it requires the network to support the framework, which complicates its deployment [54]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
