QoE-Assured 4K HTTP live streaming via transient segment holding at mobile edge

HTTP-based live streaming has become increasingly popular in recent years, and more users have started generating 4K live streams from their devices (e.g., mobile phones) through social-media service providers like Facebook or YouTube. If the audience is located far from a live stream source across the global Internet, TCP throughput becomes substantially suboptimal due to slow-start and congestion control mechanisms. This is especially the case when the end-to-end content delivery path involves radio access network (RAN) at the last mile. As a result, the data rate perceived by a mobile receiver may not meet the high requirement of 4K video streams, which causes deteriorated Quality-of-Experience (QoE). In this paper, we propose a scheme named Edge-based Transient Holding of Live sEgment (ETHLE), which addresses the issue above by performing context-aware transient holding of video segments at the mobile edge with virtualized content caching capability. Through holding the minimum number of live video segments at the mobile edge cache in a context-aware manner, the ETHLE scheme is able to achieve seamless 4K live streaming experiences across the global Internet by eliminating buffering and substantially reducing initial startup delay and live stream latency. It has been deployed as a virtual network function at an LTE-A network, and its performance has been evaluated using real live stream sources that are distributed around the world. The significance of this paper is that by leveraging on virtualized caching resources at the mobile edge, we have addressed the conventional transport-layer bottleneck and enabled QoE-assured Internet-wide live streaming to support the emerging live streaming services with high data rate requirements

Enhancing Video QoE Over High-speed Train Using Segment-based Prefetching and Caching

The big picture of 5G will bring a range of new unique service capabilities, where ensuring Quality of Experience (QoE) continuity in challenging situations such as high mobility, e.g. on-board User Equipments (UEs) in High Speed Train (HST) is one of sharp killer applications. In this paper, we propose a Mobile Edge Computing (MEC) driven solution to improve QoE, for UEs in the HST with perceived Dynamic Adaptive Streaming over HTTP (DASH) video demands. Considering the challenging wireless communication conditioning (e.g., path loss and Doppler Effect due to high mobility) between HST and Base Station (BS) along the railway for enabling progress and seamless video consuming, the case study shows the benefit of MEC functions mainly from content prefetching and complementarily from content caching, over benchmark solution where UEs solely download video segments through challenging wireless channel

QoE management of multimedia streaming services in future networks : a tutorial and survey

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Video Caching, Analytics and Delivery at the Wireless Edge: A Survey and Future Directions

Future wireless networks will provide high bandwidth, low-latency, and ultra-reliable Internet connectivity to meet the requirements of different applications, ranging from mobile broadband to the Internet of Things. To this aim, mobile edge caching, computing, and communication (edge-C3) have emerged to bring network resources (i.e., bandwidth, storage, and computing) closer to end users. Edge-C3 allows improving the network resource utilization as well as the quality of experience (QoE) of end users. Recently, several video-oriented mobile applications (e.g., live content sharing, gaming, and augmented reality) have leveraged edge-C3 in diverse scenarios involving video streaming in both the downlink and the uplink. Hence, a large number of recent works have studied the implications of video analysis and streaming through edge-C3. This article presents an in-depth survey on video edge-C3 challenges and state-of-the-art solutions in next-generation wireless and mobile networks. Specifically, it includes: a tutorial on video streaming in mobile networks (e.g., video encoding and adaptive bitrate streaming); an overview of mobile network architectures, enabling technologies, and applications for video edge-C3; video edge computing and analytics in uplink scenarios (e.g., architectures, analytics, and applications); and video edge caching, computing and communication methods in downlink scenarios (e.g., collaborative, popularity-based, and context-aware). A new taxonomy for video edge-C3 is proposed and the major contributions of recent studies are first highlighted and then systematically compared. Finally, several open problems and key challenges for future research are outlined

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

BLADE: A BitLine Accelerator for Devices on the Edge

The increasing ubiquity of edge devices in the consumer market, along with their ever more computationally expensive workloads, necessitate corresponding increases in computing power to support such workloads. In-memory computing is attractive in edge devices as it reuses preexisting memory elements, thus limiting area overhead. Additionally, in-SRAM Computing (iSC) efficiently performs computations on spatially local data found in a variety of emerging edge device workloads. We therefore propose, implement, and benchmark BLADE, a BitLine Accelerator for Devices on the Edge. BLADE is an iSC architecture that can perform massive SIMD-like complex operations on hundreds to thousands of operands simultaneously. We implement BLADE in 28nm CMOS and demonstrate its functionality down to 0.6V, lower than any conventional state-of-the-art iSC architecture. We also benchmark BLADE in conjunction with a full Linux software stack in the gem5 architectural simulator, providing a robust demonstration of its performance gain in comparison to an equivalent embedded processor equipped with a NEON SIMD co-processor. We benchmark BLADE with three emerging edge device workloads, namely cryptography, high efficiency video coding, and convolutional neural networks, and demonstrate 4x, 6x, and 3x performance improvement, respectively, in comparison to a baseline CPU/NEON processor at an equivalent power budget

Tile-based edge caching for 360° live video streaming

360° video is becoming an increasingly popular technology on commercial social platforms and vital part of emerging Virtual Reality/Augmented Reality (VR/AR) applications. However, the delivery of 360° video content in mobile networks is challenging because of its size. The encoding of 360° video into multiple quality layers and tiles and edge cache-assisted video delivery have been proposed as a remedy to the excess bandwidth requirements of 360° video delivery systems. Existing works using the above tools have shown promising performance for Video-on-Demand (VoD) 360° delivery, but they cannot be straightforwardly extended in a live-streaming setup. Motivated by the above, we study edge cache-assisted 360° live video streaming to increase the overall quality of the delivered 360° videos to users and reduce the service cost. We employ Long Short-Term Memory (LSTM) networks to forecast the evolution of the content requests and prefetch content to caches. To further enhance the delivered video quality, users located in the overlap of the coverage areas of multiple Small Base Stations (SBSs) are allowed to receive data from any of these SBSs. We evaluate and compare the performance of our algorithm with Least Frequently Used (LFU), Least Recently Used (LRU), and First In First Out (FIFO) algorithms. The results show the superiority of the proposed approach in terms of delivered video quality, cache-hit-ratio and backhaul link usage

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

5G Network Slicing using SDN and NFV: A Survey of Taxonomy, Architectures and Future Challenges

In this paper, we provide a comprehensive review and updated solutions related to 5G network slicing using SDN and NFV. Firstly, we present 5G service quality and business requirements followed by a description of 5G network softwarization and slicing paradigms including essential concepts, history and different use cases. Secondly, we provide a tutorial of 5G network slicing technology enablers including SDN, NFV, MEC, cloud/Fog computing, network hypervisors, virtual machines & containers. Thidly, we comprehensively survey different industrial initiatives and projects that are pushing forward the adoption of SDN and NFV in accelerating 5G network slicing. A comparison of various 5G architectural approaches in terms of practical implementations, technology adoptions and deployment strategies is presented. Moreover, we provide a discussion on various open source orchestrators and proof of concepts representing industrial contribution. The work also investigates the standardization efforts in 5G networks regarding network slicing and softwarization. Additionally, the article presents the management and orchestration of network slices in a single domain followed by a comprehensive survey of management and orchestration approaches in 5G network slicing across multiple domains while supporting multiple tenants. Furthermore, we highlight the future challenges and research directions regarding network softwarization and slicing using SDN and NFV in 5G networks.Comment: 40 Pages, 22 figures, published in computer networks (Open Access