On-the-Fly QoE-Aware Transcoding in the Mobile Edge

IEEE Access

5G communication brings substantial improvements in the quality of service provided to various applications by achieving higher throughput and lower latency. However, interactive multimedia applications (e.g., ultra high definition video conferencing, 3D and multiview video streaming, crowdsourced video streaming, cloud gaming, virtual and augmented reality) are becoming more ambitious with high volume and low latency video streams putting strict demands on the already congested networks. Mobile Edge Computing (MEC) is an emerging paradigm that extends cloud computing capabilities to the edge of the network i.e., at the base station level. To meet the latency requirements and avoid the end-toend communication with remote cloud data centers, MEC allows to store and process video content (e.g., caching, transcoding, pre-processing) at the base stations. Both video on demand and live video streaming can utilize MEC to improve existing services and develop novel use cases, such as video analytics, and targeted advertisements. MEC is expected to reshape the future of video streaming by providing ultra-reliable and low latency streaming (e.g., in augmented reality, virtual reality, and autonomous vehicles), pervasive computing (e.g., in real-time video analytics), and blockchain-enabled architecture for secure live streaming. This paper presents a comprehensive survey of recent developments in MEC-enabled video streaming bringing unprecedented improvement to enable novel use cases. A detailed review of the state-of-the-art is presented covering novel caching schemes, optimal computation offloading, cooperative caching and offloading and the use of artificial intelligence (i.e., machine learning, deep learning, and reinforcement learning) in MEC-assisted video streaming services. INDEX TERMS Live streaming, machine learning, mobile edge computing, VoD, video Streaming.

IEEE Transactions on Multimedia

Internet video streaming applications have been demanding more bandwidth and higher video quality, especially with the advent of Virtual Reality (VR) and Augmented Reality (AR) applications. While adaptive streaming protocols like MPEG-DASH (Dynamic Adaptive Streaming over HTTP) allows video quality to be flexibly adapted, e.g., degraded when mobile network condition deteriorates, this is not an option if the application itself requires guaranteed 4K quality at all time. On the other hand, conventional end-to-end TCP has been struggling in supporting 4K video delivery across long-distance Internet paths containing both fixed and mobile network segments with heterogeneous characteristics. In this paper, we present a novel and practically-feasible system architecture named MVP (Mobile edge Virtualization with adaptive Prefetching), which enables content providers to embed their content intelligence as a virtual network function (VNF) into the mobile network operator's (MNO) infrastructure edge. Based on this architecture, we present a context-aware adaptive video prefetching scheme in order to achieve QoE-assured 4K video on demand (VoD) delivery across the global Internet. Through experiments based on a real LTE-A network infrastructure, we demonstrate that our proposed scheme is able to achieve QoE-assured 4K VoD streaming, especially when the video source is located remotely in the public Internet, in which case none of the state-of-the-art solutions is able to support such an objective at global Internet scale.

IEEE Access, 2018

Online video broadcasting platforms are distributed, complex, cloud oriented, scalable, microservice-based systems that are intended to provide over-the-top and live content to audience in scattered geographic locations. Due to the nature of cloud VM hosting costs, the subscribers are usually served under limited resources in order to minimize delivery budget. However, operations including transcoding require high-computational capacity and any disturbance in supplying requested demand might result in quality of experience (QoE) deterioration. For any online delivery deployment, understanding user's QoE plays a crucial role for rebalancing cloud resources. In this paper, a methodology for estimating QoE is provided for a scalable cloud-based online video platform. The model will provide an adeptness guideline regarding limited cloud resources and relate computational capacity, memory, transcoding and throughput capability, and finally latency competence of the cloud service to QoE. Scalability and efficiency of the system are optimized through reckoning sufficient number of VMs and containers to satisfy the user requests even on peak demand durations with minimum number of VMs. Both horizontal and vertical scaling strategies (including VM migration) are modeled to cover up availability and reliability of intermediate and edge content delivery network cache nodes.

2018

Recently, the growing demand for rich multimedia content such as Video on Demand (VoD) has made the data transmission from content delivery networks (CDN) to end-users quite challenging. Edge networks have been proposed as an extension to CDN networks to alleviate this excessive data transfer through caching and to delegate the computation tasks to edge servers. To maximize the caching efficiency in the edge networks, different Mobile Edge Computing (MEC) servers assist each others to effectively select which content to store and the appropriate computation tasks to process. In this paper, we adopt a collaborative caching and transcoding model for VoD in MEC networks. However, unlike other models in the literature, different chunks of the same video are not fetched and cached in the same MEC server. Instead, neighboring servers will collaborate to store and transcode different video chunks and consequently optimize the limited resources usage. Since we are dealing with chunks cachin...

International Journal of Research Publication and Reviews, 2025

The rise in demand for low-latency, high-resolution video streaming has brought to light fundamental shortcomings in traditional Dynamic Adaptive Streaming over HTTP (MPEG-DASH) systems under mobile and bandwidth-limited conditions. Traditional CDN-based architectures are usually plagued by large startup delays, re-buffering, and bitrate switching caused by centralized processing and narrow contextual awareness. This paper introduces an end-to-end edge-centric framework that re-architects the DASH pipeline to take advantage of cutting-edge edge computing features. The framework combines just-in-time transcoding, dynamic edge-generated MPDs, ABR advisories based on reinforcement learning, predictive prefetching, and low-latency chunk delivery under a common architecture. It also includes a continuous QoE (Quality of Experience) feedback loop to adapt strategies in real-time. By moving content processing and decisionmaking nearer to end users, the new design is expected to lower latency, increase quality stability, and improve overall user experience. Apart from its technical merit, this framework has the importance of being society-oriented as it facilitates equal access to quality video services even in areas with scarce network infrastructure. It facilitates remote learning, telemedicine, and digital accessibility by ensuring guaranteed media delivery in multivarious and underprivileged environments. Finally, the suggested system not only solves the technical hurdles of conventional streaming but also encourages communities through better access to digital content, leading to a more connected and well-informed society.

2021 IEEE 46th Conference on Local Computer Networks (LCN), 2021

Recent studies have revealed that network-assisted techniques, by providing a comprehensive view of the network, improve HTTP Adaptive Streaming (HAS) system performance significantly. This paper leverages the capability of Software-Defined Networking, Network Function Virtualization, and edge computing to introduce a CDN-Aware QoE Optimization in SDN-Assisted Adaptive Video Streaming (CSDN) framework. We employ virtualized edge entities to collect various information items and run an optimization model with a new server/segment selection approach in a time-slotted fashion to serve the clients' requests by selecting optimal cache servers. In case of a cache miss, a client's request is served by an optimal replacement quality from a cache server, by a quality transcoded from an optimal replacement quality at the edge, or by the originally requested quality from the origin server. Comprehensive experiments conducted on a large-scale testbed demonstrate that CSDN outperforms other approaches in terms of the users' QoE and network utilization.

2017

1M.Tech Computer Network Bhilai Institute of Technology, Durg (C.G.) – 491001 India 2Prof. Department of Computer Science and Engineering Bhilai Institute of Technology, Durg (C.G.) – 491001 India ----------------------------------------------------------------------------***--------------------------------------------------------------Abstract— In present day to day, content is shifting paradigm towards additional interactive media, principally video. Availableness of assorted transmission formats, and devices in mobile, IPTV conjointly demands for specific necessities on content delivery networks, parturition importance on value effectiveness of feeding content to ever growing world net traffic, during this paper we tend to discuss numerous strategies of enhancing the value potency, through use of accommodative video streaming and proper orchestration of storage, information measure and computing resources. Main objective is to formulate associate optimization downside from the co...

Proceedings of the 29th ACM International Conference on Multimedia

The Multi-access Edge Computing (MEC) paradigm oers cloudcomputing support to rich media applications, including Dynamic Adaptive Streaming over HTTP (DASH)-based ones at the edge of the network, close to mobile users. MEC servers, typically deployed at base stations (BS), help reduce latency and improve quality of experience (QoE) of video streaming. Unfortunately the communications involving mobile users require handovers between BSs and these inuence both transmission eciency because of the relative position of the MEC servers and transit cost. At the same time, serving MEC for a mobile user should not necessarily be changed when handover occurs. This paper introduces QoE Ready to Respond (QoE-R2R), a QoE-aware MEC Selection scheme for DASH-based mobile adaptive video streaming for optimizing video transmission in a MEC-supported network environment. Simulation-based testing shows that the proposed (QoE-R2R) scheme outperforms some traditional alternative solutions. Compared to hit rate and delay-based schemes, QoE-R2R reduces by 27.6% transmission time and improves with 6.2% QoE. CCS CONCEPTS • Networks ! Mobile networks; In-network processing; • Information systems ! Multimedia streaming.

2020

Multi-Access Edge Computing (MEC) adaptive streaming presents an opportunity to jointly optimize the quality of experience in cellular networks by moving the adaptation intelligence from the client to the edge cloud. In this paper, we investigate the performance of MEC-assisted algorithms and compare their performance with the client based adaptation logic. We conduct extensive experiments and quantify benefits and drawbacks of edge computing-assisted adaptation algorithms. The results from our experiments reveal that MEC-assisted algorithms outperforms the purely client-based heuristics in most of the video quality metrics. However, the results also show that the MEC-assisted algorithms are not able to protect the playback buffer from drying up under different network settings.

Cluster Computing, 2018

Nowadays, more than 75% of Internet traffic is multimedia traffic, moreover mobile traffic is growing at a rate of 50% each year. All these data together with the evolution of the cloud infrastructures lead us to develop Cloud Mobile Media (CMM) architectures to support the needs demanded by end users. Nevertheless, due to an inherit higher and variable end to end delay mainly as a result of the virtualization process, new challenges appear in particular for live video streaming applications in order to keep a good Quality of Experience (QoE) of the delivered video. Thus, to keep client's satisfaction within good levels in terms of Mean Opinion Score (MOS), we propose an adaptive QoE-based architecture running on CMM infrastructures for live streaming services. In order to carry out this goal, we propose an estimation of MOS values using an statistical method based on factor analysis. This estimation is based on different measured variables throughout the CMM infrastructure. In addition, we compare the accuracy of the estimated MOS against well-known publicly available video quality algorithms. With these estimations, our proposal is based on two added controllers to the CMM infrastructure: (a) the Software Defined Network controller that acts as a master and (b) the Media Streamer controller. Each one does different actions on the CMM infrastructure in order to maintain and improve the QoE at each end user. Finally, this architecture has been implemented over a fat tree topology in order to show their functionality. The results show that our proposal works properly and it adapts quickly to the network changes in order to deliver a good MOS.