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.
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.
@cite_24 @cite_35
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Experimental data are presented that clearly demonstrate the scope of application of peak signal-to-noise ratio (PSNR) as a video quality metric. It is shown that as long as the video content and the codec type are not changed, PSNR is a valid quality measure. However, when the content is changed, correlation between subjective quality and PSNR is highly reduced. Hence PSNR cannot be a reliable method for assessing the video quality across different video contents. Omnidirectional videos of real world environments viewed on head-mounted displays with real-time head motion tracking can offer immersive visual experiences. For live streaming applications, compression is critical to reduce the bitrate. Omnidirectional videos, which are spherical in nature, are mapped onto one or more planes before encoding to interface with modern video coding standards. In this paper, we consider the problem of evaluating the coding efficiency in the context of viewing with a head-mounted display. We extract viewport based head motion trajectories, and compare the original and coded videos on the viewport. With this approach, we compare different sphere-to-plane mappings. We show that the average viewport quality can be approximated by a weighted spherical PSNR.