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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Evaluation of coding efficiency is traditionally modeled as a continuous rate-distortion (R-D) function, where the peak signal-to-noise ratio (PSNR) is adopted as the quality measure. Although the PSNR-versus-bitrate curve offers some useful tradeoff information between video quality and coding bit-rates, it does not take human perceptual experience into account. In this work, by following the recent image video quality assessment framework based on the just-noticeable-difference (JND) notion, we conduct a subjective test for HEVC (High Efficiency Video Codec) video to measure the QP value that lies in the boundary of perceptually lossless and lossy coded bit streams for each human subject. This is also known as the first JND point. It is observed that the statistics of the first JND points of 30 subjects follows the normal distribution for a great majority of test sequences. Finally, a machine-learning approach is proposed to predict the mean of the group-based JND distribution based on extracted video features. It is shown by experimental results that the mean JND point can be predicted accurately. Simple quality metrics such as PSNR are known to not correlate well with subjective quality when tested across a wide spectrum of video content or quality regime. Recently, efforts have been made in designing objective quality metrics trained on subjective data, demonstrating better correlation with video quality perceived by human. Clearly, the accuracy of such a metric heavily depends on the quality of the subjective data that it is trained on. In this paper, we propose a new approach to recover subjective quality scores from noisy raw measurements, by jointly estimating the subjective quality of impaired videos, the bias and consistency of test subjects, and the ambiguity of video contents all together. Compared to previous methods which partially exploit the subjective information, our approach is able to exploit the information in full, yielding better handling of outliers without the need for z-scoring or subject rejection. It also handles missing data more gracefully. Lastly, as side information, it provides interesting insights on the test subjects and video contents. A continuous evaluation of the end user’s quality-of-experience (QoE) is essential for efficient video streaming. This is crucial for networks with constrained resources that offer time-varying channel quality to its users. In hypertext transfer protocol-based video streaming, the QoE is measured by quantifying the perceptual impact of distortions caused by rate adaptation or interruptions in playback due to rebuffering events. The resulting impact on the QoE due to these distortions has been studied individually in the literature. However, the QoE is determined by an interplay of these distortions, and therefore necessitates a combined study of them. To the best of our knowledge, there is no publicly available database that studies these distortions jointly on a continuous time basis. In this paper, our contributions are twofold. First, we present a database consisting of videos at full high definition and ultrahigh definition resolutions. We consider various levels of rate adaptation and rebuffering distortions together in these videos as experienced in a typical realistic setting. A subjective evaluation of these videos is conducted on a continuous time scale. Second, we present a QoE evaluation framework comprising a learning-based model during playback and an exponential model during rebuffering. Furthermore, we perform an objective evaluation of popular video quality assessment and continuous time QoE metrics over the constructed database. The objective evaluation study demonstrates that the performance of the proposed QoE model is superior to that of the objective metrics. The database is publicly available for download at http: www.iith.ac.in lfovia downloads.html . In recent years, quality of experience (QoE) has been investigated and proved to have both influential factors on user's visual quality and perceptual quality, while the perceptual quality means user's requirement on personalized content should be acquired in optimized quality. That's to say, those segments holding user interested content such as highlights need to be allocated more network resource in a resource-limited streaming scenario. However, all the existing HTTP-based adaptive methods only focus the content-agnostic bitrate adaptation according to limited network resources or energy resource, since they ignored user perceived semantics on some important segments, which suffered less quality on the important segments than on those ordinary ones, so as to hurt the overall QoE. In this paper, we have proposed a new semantic-aware adaptation scheme for MPEG-DASH services, which decides how to preserve bandwidth and buffering time depending on content descriptors for the perceived important content to users. Further, a semantic-aware probe and adaptation (SMA-PANDA) algorithm has been implemented in a DASH client to compare with conventional bitrate adaptions. Preliminary results show that SMA-PANDA achieves better QoE and flexibility on streaming user's interested content on MPEG-DASH platform, and it also aggressively helps user interested content compete more resource to deliver high quality presentation.