text: The recent surge of applications involving the use of @math video challenges mobile networks infrastructure, as @math video files are of significant size, and current delivery and edge caching architectures are unable to guarantee their timely delivery. In this paper, we investigate the problem of joint collaborative content-aware caching and delivery of @math videos. The proposed scheme takes advantage of @math video encoding in multiple tiles and layers to make fine-grained decisions regarding which tiles to cache in each Small Base Station (SBS), and from where to deliver them to the end users, as users may reside in the coverage area of multiple SBSs. This permits to cache the most popular tiles in the SBSs, while the remaining tiles may be obtained through the backhaul. In addition, we explicitly consider the time delivery constraints to ensure continuous video playback. To reduce the computational complexity of the optimization problem, we simplify it by introducing a fairness constraint. This allows us to split the original problem into subproblems corresponding to Groups of Pictures (GoP). Each of the subproblems is then decoupled into their caching and routing components and solved with the method of Lagrange partial relaxation. Finally, we evaluate the performance of the proposed method for various system parameters and compare it with schemes that do not consider @math video encoding into multiple tiles and quality layers. The results make clear the benefits coming from caching and delivery decisions on per tile basis.
  @cite_24: Currently, more and more 360-degree videos (or 360 videos for short) are being provided via the Internet. This kind of videos can render a virtual reality (VR) environment via a head-mounted display (HMD). However, understanding the quality of experience (QoE) of 360 videos is a big challenge because user experience in VR is a very complex phenomenon. In this paper, the QoE of 360 videos is considered in terms of four aspects, namely perceptual quality, presence, acceptability, and cybersickness. Subjective tests are designed to investigate the influences of important factors including encoding parameters, content characteristics, and device types on QoE aspects. In addition, a comparison of perceptual quality and acceptability between VR and non-VR rendering modes, which refer to watching 360 videos with and without using an HMD respectively, is also made in this study. To the best of our knowledge, this is the first study that covers these four QoE aspects and a large number of influence factors.
  @cite_35: 360 videos are becoming more and more popular on video streaming platforms. However, a good quality metric for 360 videos is still an open issue. In this work, we investigate both objective and subjective quality metrics for 360 videos. The goals are to understand the perceived quality range provided by existing mobile 360 videos and, especially, to identify appropriate objective quality metrics for 360 video communications. To that end, a subjective test is conducted in this study. Then, the relationship between objective quality and subjective quality is investigated. Especially, ten objective quality measures are computed, considering the coding distortion measurement, cross-format distortion measurement, and end-to-end distortion measurement. It is found that most of the objective quality measures are well correlated with subjective quality. Also, among the evaluated quality measures, PSNR is shown to be the most appropriate for 360 video communications.
summary: In @cite_24 , QoE for streaming of @math videos is studied in terms of: a) perceptual quality, b) presence, c) acceptability, and d) cyber-sickness. In particular, perceptual quality and the acceptability are measured when users watch a @math video both with or without a Head-Mounted Display (HMD). Similarly, in @cite_35 , a number of objective quality metrics, including PSNR, weighted to spherically uniform PSNR (WS-PSNR), spherical PSNR without interpolation (S-PSNR-NN), spherical PSNR with interpolation (S-PSNR-I), and PSNR in Crasters Parabolic Projection (CPP-PSNR) are explored for quantifying the quality of @math video encoding. It is concluded that traditional PSNR is the most appropriate quality metric for measuring end-to-end distortion because of its low complexity.