Delivery of 360° videos in edge caching assisted wireless cellular networks

In recent years, 360° videos have become increasingly popular on commercial social platforms, and are a vital part of emerging Virtual Reality (VR) applications. However, the delivery of 360° videos requires significant bandwidth resources, which makes streaming of such data on mobile networks challenging. The bandwidth required for delivering 360° videos can be reduced by exploiting the fact that users are interested in viewing only a part of the video scene, the requested viewport. As different users may request different viewports, some parts of the 360° scenes may be more popular than others. 360° video delivery on mobile networks can be facilitated by caching popular content at edge servers, and delivering it from there to the users. However, existing edge caching schemes do not take full potential of the unequal popularity of different parts of a video, which renders them inefficient for caching 360° videos. Inspired by the above, in this thesis, we investigate how advanced 360° video coding tools, i.e., encoding into multiple quality layers and tiles, can be utilized to build more efficient wireless edge caching schemes for 360° videos. The above encoding allows the caching of only the parts of the 360° videos that are popular in high quality. To understand how edge caching schemes can benefit from 360° video coding, we compare the caching of 360° videos encoded into multiple quality layers and tiles with layer-agnostic and tile-agnostic schemes. To cope with the fact that the content popularity distribution may be unknown, we use machine learning techniques, for both Video on Demand (VoD), and live streaming scenarios. From our findings, it is clear that by taking into account the aforementioned 360° video characteristics leads to an increased performance in terms of the quality of the video delivered to the users, and the usage of the backhaul links

Machine Learning for Multimedia Communications

Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the learning-based architectures enables us to accurately model the image and video behavior such that tremendous compression gains can be achieved. Similarly, error concealment, streaming strategy or even user perception modeling have widely benefited from the recent learningoriented developments. However, learning-based algorithms often imply drastic changes to the way data are represented or consumed, meaning that the overall pipeline can be affected even though a subpart of it is optimized. In this paper, we review the recent major advances that have been proposed all across the transmission chain, and we discuss their potential impact and the research challenges that they raise

Communication, Computing and Caching for Mobile VR Delivery: Modeling and Trade-off

Mobile virtual reality (VR) delivery is gaining increasing attention from both industry and academia due to its ability to provide an immersive experience. However, achieving mobile VR delivery requires ultra-high transmission rate, deemed as a first killer application for 5G wireless networks. In this paper, in order to alleviate the traffic burden over wireless networks, we develop an implementation framework for mobile VR delivery by utilizing caching and computing capabilities of mobile VR device. We then jointly optimize the caching and computation offloading policy for minimizing the required average transmission rate under the latency and local average energy consumption constraints. In a symmetric scenario, we obtain the optimal joint policy and the closed-form expression of the minimum average transmission rate. Accordingly, we analyze the tradeoff among communication, computing and caching, and then reveal analytically the fact that the communication overhead can be traded by the computing and caching capabilities of mobile VR device, and also what conditions must be met for it to happen. Finally, we discuss the optimization problem in a heterogeneous scenario, and propose an efficient suboptimal algorithm with low computation complexity, which is shown to achieve good performance in the numerical results.Comment: to appear in IEEE ICC 201

EFFECT ON 360 DEGREE VIDEO STREAMING WITH CACHING AND WITHOUT CACHING

People all around the world are becoming more and more accustomed to watching 360-degree videos, which offer a way to experience virtual reality. While watching videos, it enables users to view video scenes from any perspective. To reduce bandwidth costs and provide the video with less latency, 360-degree video caching at the edge server may be a smart option. A hypothetical 360-degree video streaming system can partition popular video materials into tiles that are cached at the edge server. This study uses the Least Recently Used (LRU) and Least Frequently Used (LFU) algorithms to accomplish video caching and suggest a system architecture for 360-degree video caching. Two 360-degree videos from 48 users\u27 head movements are used in the experiment, and caching between the LRU cache and LFU cache is compared by changing the cache size. The findings demonstrate that, for varied cache sizes, utilizing LFU caching outperforms LRU caching in terms of average cache hit rate. In the first part of the research, we compared LRU and LFU caching algorithm. In the second part of the research, a suitable caching strategy model was developed based on user’s field of view. Field of view (FoV) is the term used to describe the portion of the 3600 videos that viewers typically see when watching 3600 videos. Edge caching can be a smart way to increase customer satisfaction while maximizing bandwidth usage (QoE). A 3600-video caching strategy has been developed in this study using three machine learning models that use random forest, linear regression, and Bayesian regression. As features, tiles\u27 frequency, user\u27s view prediction probability, and resolution were used. The created machine learning models are designed to decide the caching method for 360-degree video tiles. The models can forecast the frequency of viewing for 3600 video tiles (subsets of a full video). With a predictive R2 value of 0.79, the random forest regression model performs better than the other suggested models when the outcomes of the three developed models are compared. In the third part of the research, to compare our machine learning algorithm with LRU algorithm, a python test bench program was written to evaluate both algorithms on the test set by varying the cache size. The results demonstrate that our machine learning approach, which was created for 360-degree video caching, outperforms the LRU algorithm