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Ten Ways in which Virtual Reality Differs from Video Streaming
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Abstract
Virtual Reality (VR) applications have a number of unique characteristics that set them apart from traditional video streaming. These characteristics have major implications on the design of VR rendering, adaptation, prefetching, caching, and transport mechanisms. This paper contrasts VR to video streaming, stored 2D video streaming in particular, and discusses how to rethink system and network support for VR.
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Immersive virtual reality (VR) emerges as a promising research and clinical tool. However, several studies suggest that VR induced adverse symptoms and effects (VRISE) may undermine the health and safety standards, and the reliability of the scientific results. In the current literature review, the technical reasons for the adverse symptomatology are investigated to provide suggestions and technological knowledge for the implementation of VR head-mounted display (HMD) systems in cognitive neuroscience. The technological systematic literature indicated features pertinent to display, sound, motion tracking, navigation, ergonomic interactions, user experience, and computer hardware that should be considered by the researchers. Subsequently, a meta-analysis of 44 neuroscientific or neuropsychological studies involving VR HMD systems was performed. The meta-analysis of the VR studies demonstrated that new generation HMDs induced significantly less VRISE and marginally fewer dropouts. Importantly, the commercial versions of the new generation HMDs with ergonomic interactions had zero incidents of adverse symptomatology and dropouts. HMDs equivalent to or greater than the commercial versions of contemporary HMDs accompanied with ergonomic interactions are suitable for implementation in cognitive neuroscience. In conclusion, researchers' technological competency, along with meticulous methods and reports pertinent to software, hardware, and VRISE, are paramount to ensure the health and safety standards and the reliability of neuroscientific results.
Streaming 360° videos requires more bandwidth than non-360° videos. This is because current solutions assume that users perceive the quality of 360° videos in the same way they perceive the quality of non-360° videos. This means the bandwidth demand must be proportional to the size of the user's field of view. However, we found several quality-determining factors unique to 360° videos, which can help reduce the bandwidth demand. They include the moving speed of a user's viewpoint (center of the user's field of view), the recent change of video luminance, and the difference in depth-of-fields of visual objects around the viewpoint.
This paper presents Pano, a 360° video streaming system that leverages the 360° video-specific factors. We make three contributions. (1) We build a new quality model for 360° videos that captures the impact of the 360° video-specific factors. (2) Pano proposes a variable-sized tiling scheme in order to strike a balance between the perceived quality and video encoding efficiency. (3) Pano proposes a new quality-adaptation logic that maximizes 360° video user-perceived quality and is readily deployable. Our evaluation (based on user study and trace analysis) shows that compared with state-of-the-art techniques, Pano can save 41-46% bandwidth without any drop in the perceived quality, or it can raise the perceived quality (user rating) by 25%-142% without using more bandwidth.
We present our experience with QUIC, an encrypted, multiplexed, and low-latency transport protocol designed from the ground up to improve transport performance for HTTPS traffic and to enable rapid deployment and continued evolution of transport mechanisms. QUIC has been globally deployed at Google on thousands of servers and is used to serve traffic to a range of clients including a widely-used web browser (Chrome) and a popular mobile video streaming app (YouTube). We estimate that 7% of Internet traffic is now QUIC. We describe our motivations for developing a new transport, the principles that guided our design, the Internet-scale process that we used to perform iterative experiments on QUIC, performance improvements seen by our various services, and our experience deploying QUIC globally. We also share lessons about transport design and the Internet ecosystem that we learned from our deployment.
This article is summarized in: the morning paper
an interesting/influential/important paper from the world of CS every weekday morning, as selected by Adrian Colyer
We consider the problem of fair resource allocation in a system containing different resource types, where each user may have different demands for each resource. To address this problem, we propose Dominant Resource Fairness (DRF), a generalization of max-min fairness to multiple resource types. We show that DRF, unlike other possible policies, satisfies several highly desirable properties. First, DRF incentivizes users to share re-sources, by ensuring that no user is better off if resources are equally partitioned among them. Second, DRF is strategy-proof, as a user cannot increase her allocation by lying about her requirements. Third, DRF is envy-free, as no user would want to trade her allocation with that of another user. Finally, DRF allocations are Pareto efficient, as it is not possible to improve the allocation of a user without decreasing the allocation of another user. We have implemented DRF in the Mesos cluster resource manager, and show that it leads to better throughput and fairness than the slot-based fair sharing schemes in cur-rent cluster schedulers.
Virtual reality (VR) is receiving attention enough to be considered as its revival age in both industrial and academic field. Since VR systems have various types of interaction with users and new types of interaction are constantly being developed, various studies investigating user experience (UX) of VR systems are continuously needed. However, there is still a lack of research on the taxonomy that can recognize the main characteristics of VR system at a glance by reflecting the influencing factors of UX. Therefore, we collected and reviewed the research related to the UX evaluation of the VR system in order to identify the current research status and to suggest future research direction. To achieve this, a systematic review was conducted on UX studies for VR, and taxonomies of VR system including influencing factors of UX were proposed. A total of 393 unique articles were collected, and 65 articles were selected to be reviewed via Systematic Reviews and Meta-Analyses methodology. The selected articles were analyzed according to predefined taxonomies. As a result, current status of research can be identified base on the proposed taxonomies. Besides, issues related to VR devices and technology, and research method for future research directions can be suggested.
Flare is a practical system for streaming 360-degree videos on commodity mobile devices. It takes a viewport-adaptive approach, which fetches only portions of a panoramic scene that cover what a viewer is about to perceive. We conduct an IRB-approved user study where we collect head movement traces from 130 diverse users to gain insights on how to design the viewport prediction mechanism for Flare. We then develop novel online algorithms that determine which spatial portions to fetch and their corresponding qualities. We also innovate other components in the streaming pipeline such as decoding and server-side transmission. Through extensive evaluations (~400 hours' playback on WiFi and ~100 hours over LTE), we show that Flare significantly improves the QoE in real-world settings. Compared to non-viewport-adaptive approaches, Flare yields up to 18x quality level improvement on WiFi, and achieves high bandwidth reduction (up to 35%) and video quality enhancement (up to 4.9x) on LTE.
The popularity of 360° videos has grown rapidly due to the immersive user experience. 360° videos are displayed as a panorama and the view automatically adapts with the head movement. Existing systems stream 360° videos in a similar way as regular videos, where all data of the panoramic view is transmitted. This is wasteful since a user only views a small portion of the 360° view. To save bandwidth, recent works propose the tile-based streaming, which divides the panoramic view to multiple smaller sized tiles and streams only the tiles within a user's field of view (FoV) predicted based on the recent head position. Interestingly, the tile-based streaming has only been simulated or implemented on desktops. We find that it cannot run in real-time even on the latest smartphone (e.g., Samsung S7, Samsung S8 and Huawei Mate 9) due to hardware and software limitations. Moreover, it results in significant video quality degradation due to head movement prediction error, which is hard to avoid. Motivated by these observations, we develop a novel tile-based layered approach to stream 360° content on smartphones to avoid bandwidth wastage while maintaining high video quality. Through real system experiments, we show our approach can achieve up to 69% improvement in user QoE and 49% in bandwidth savings over existing approaches. To the best of our knowledge, this is the first 360° streaming framework that takes into account the practical limitations of Android based smartphones.
Objective methods for assessing perceptual image quality have traditionally attempted to quantify the visibility of errors between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a Structural Similarity Index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. A MatLab implementation of the proposed algorithm is available online at http://www.cns.nyu.edu/~lcv/ssim/.
We examine the effect that variations in the temporal quality of videos have on global video quality. We also propose a general framework for constructing temporal video quality assessment (QA) algorithms that seek to assess transient temporal errors, such as packet losses. The proposed framework modifies simple frame-based quality assessment algorithms by incorporating a temporal quality variance factor. We use packet loss from channel errors as a specific study of practical significance. Using the PSNR and the SSIM index as exemplars, we are able to show that the new video QA algorithms are highly responsive to packet loss errors.
Robust 360 degree video streaming via non-linear sampling
- R Mijanur
- Voicu Palash
- Popescu
Mijanur R. Palash, Voicu Popescu, Amit Sheoran, and Sonia Fahmy.
Robust 360 degree video streaming via non-linear sampling. In Proceedings of INFOCOM, May 2021.
A day in the life of Netflix streaming: A conversation about Netflix adaptive streaming and more
- Renata Teixeira
- Te-Yuan Huang
Renata Teixeira and Te-Yuan Huang. A day in the life of Netflix
streaming: A conversation about Netflix adaptive streaming and more.
https://networkingchannel.eu/a-day-in-the-life-of-netflix-streaming-aconversation-about-netflix-adaptive-streaming-and-more-downloads/,
2021.