40 research outputs found
Cooperative Multi-Bitrate Video Caching and Transcoding in Multicarrier NOMA-Assisted Heterogeneous Virtualized MEC Networks
Cooperative video caching and transcoding in mobile edge computing (MEC)
networks is a new paradigm for future wireless networks, e.g., 5G and 5G
beyond, to reduce scarce and expensive backhaul resource usage by prefetching
video files within radio access networks (RANs). Integration of this technique
with other advent technologies, such as wireless network virtualization and
multicarrier non-orthogonal multiple access (MC-NOMA), provides more flexible
video delivery opportunities, which leads to enhancements both for the
network's revenue and for the end-users' service experience. In this regard, we
propose a two-phase RAF for a parallel cooperative joint multi-bitrate video
caching and transcoding in heterogeneous virtualized MEC networks. In the cache
placement phase, we propose novel proactive delivery-aware cache placement
strategies (DACPSs) by jointly allocating physical and radio resources based on
network stochastic information to exploit flexible delivery opportunities.
Then, for the delivery phase, we propose a delivery policy based on the user
requests and network channel conditions. The optimization problems
corresponding to both phases aim to maximize the total revenue of network
slices, i.e., virtual networks. Both problems are non-convex and suffer from
high-computational complexities. For each phase, we show how the problem can be
solved efficiently. We also propose a low-complexity RAF in which the
complexity of the delivery algorithm is significantly reduced. A Delivery-aware
cache refreshment strategy (DACRS) in the delivery phase is also proposed to
tackle the dynamically changes of network stochastic information. Extensive
numerical assessments demonstrate a performance improvement of up to 30% for
our proposed DACPSs and DACRS over traditional approaches.Comment: 53 pages, 24 figure
A Survey on Mobile Edge Computing for Video Streaming : Opportunities and Challenges
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, crowd-sourced 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-to-end 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.publishedVersionPeer reviewe
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
Quality of experience-centric management of adaptive video streaming services : status and challenges
Video streaming applications currently dominate Internet traffic. Particularly, HTTP Adaptive Streaming ( HAS) has emerged as the dominant standard for streaming videos over the best-effort Internet, thanks to its capability of matching the video quality to the available network resources. In HAS, the video client is equipped with a heuristic that dynamically decides the most suitable quality to stream the content, based on information such as the perceived network bandwidth or the video player buffer status. The goal of this heuristic is to optimize the quality as perceived by the user, the so-called Quality of Experience (QoE). Despite the many advantages brought by the adaptive streaming principle, optimizing users' QoE is far from trivial. Current heuristics are still suboptimal when sudden bandwidth drops occur, especially in wireless environments, thus leading to freezes in the video playout, the main factor influencing users' QoE. This issue is aggravated in case of live events, where the player buffer has to be kept as small as possible in order to reduce the playout delay between the user and the live signal. In light of the above, in recent years, several works have been proposed with the aim of extending the classical purely client-based structure of adaptive video streaming, in order to fully optimize users' QoE. In this article, a survey is presented of research works on this topic together with a classification based on where the optimization takes place. This classification goes beyond client-based heuristics to investigate the usage of server-and network-assisted architectures and of new application and transport layer protocols. In addition, we outline the major challenges currently arising in the field of multimedia delivery, which are going to be of extreme relevance in future years
Sl-EDGE: Network Slicing at the Edge
Network slicing of multi-access edge computing (MEC) resources is expected to
be a pivotal technology to the success of 5G networks and beyond. The key
challenge that sets MEC slicing apart from traditional resource allocation
problems is that edge nodes depend on tightly-intertwined and
strictly-constrained networking, computation and storage resources. Therefore,
instantiating MEC slices without incurring in resource over-provisioning is
hardly addressable with existing slicing algorithms. The main innovation of
this paper is Sl-EDGE, a unified MEC slicing framework that allows network
operators to instantiate heterogeneous slice services (e.g., video streaming,
caching, 5G network access) on edge devices. We first describe the architecture
and operations of Sl-EDGE, and then show that the problem of optimally
instantiating joint network-MEC slices is NP-hard. Thus, we propose
near-optimal algorithms that leverage key similarities among edge nodes and
resource virtualization to instantiate heterogeneous slices 7.5x faster and
within 0.25 of the optimum. We first assess the performance of our algorithms
through extensive numerical analysis, and show that Sl-EDGE instantiates slices
6x more efficiently then state-of-the-art MEC slicing algorithms. Furthermore,
experimental results on a 24-radio testbed with 9 smartphones demonstrate that
Sl-EDGE provides at once highly-efficient slicing of joint LTE connectivity,
video streaming over WiFi, and ffmpeg video transcoding
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
Architectures and Algorithms for Content Delivery in Future Networks
Traditional Content Delivery Networks (CDNs) built with traditional Internet technology are
less and less able to cope with today’s tremendous content growth. Enhancing infrastructures
with storage and computation capabilities may help to remedy the situation. Information-Centric
Networks (ICNs), a proposed future Internet technology, unlike the current Internet, decouple
information from its sources and provide in-network storage. However, content delivery over in-network
storage-enabled networks still faces significant issues, such as the stability and accuracy
of estimated bitrate when using Dynamic Adaptive Streaming (DASH). Still Implementing new
infrastructures with in-network storage can lead to other challenges. For instance, the extensive
deployment of such networks will require a significant upgrade of the installed IP infrastructure.
Furthermore, network slicing enables services and applications with very different characteristics
to co-exist on the same network infrastructure.
Another challenge is that traditional architectures cannot meet future expectations for streaming
in terms of latency and network load when it comes to content, such as 360° videos and immersive
services. In-Network Computing (INC), also known as Computing in the Network (COIN), allows
the computation tasks to be distributed across the network instead of being computed on servers to
guarantee performance. INC is expected to provide lower latency, lower network traffic, and higher
throughput. Implementing infrastructures with in-network computing will help fulfill specific
requirements for streaming 360° video streaming in the future. Therefore, the delivery of 360° video and immersive services can benefit from INC.
This thesis elaborates and addresses the key architectural and algorithmic research challenges
related to content delivery in future networks. To tackle the first challenge, we propose algorithms
for solving the inaccuracy of rate estimation for future CDNs implementation with in-network
storage (a key feature of future networks). An algorithm for implementing in-network storage
in IP settings for CDNs is proposed for the second challenge. Finally, for the third challenge,
we propose an architecture for provisioning INC-enabled slices for 360° video streaming in next-generation
networks. We considered a P4-enabled Software-Defined network (SDN) as the physical
infrastructure and significantly reduced latency and traffic load for video streaming
Digital Twin-Based Network Management for Better QoE in Multicast Short Video Streaming
Multicast short video streaming can enhance bandwidth utilization by enabling
simultaneous video transmission to multiple users over shared wireless
channels. The existing network management schemes mainly rely on the sequential
buffering principle and general quality of experience (QoE) model, which may
deteriorate QoE when users' swipe behaviors exhibit distinct spatiotemporal
variation. In this paper, we propose a digital twin (DT)-based network
management scheme to enhance QoE. Firstly, user status emulated by the DT is
utilized to estimate the transmission capabilities and watching probability
distributions of sub-multicast groups (SMGs) for an adaptive segment buffering.
The SMGs' buffers are aligned to the unique virtual buffers managed by the DT
for a fine-grained buffer update. Then, a multicast QoE model consisting of
rebuffering time, video quality, and quality variation is developed, by
considering the mutual influence of segment buffering among SMGs. Finally, a
joint optimization problem of segment version selection and slot division is
formulated to maximize QoE. To efficiently solve the problem, a
data-model-driven algorithm is proposed by integrating a convex optimization
method and a deep reinforcement learning algorithm. Simulation results based on
the real-world dataset demonstrate that the proposed DT-based network
management scheme outperforms benchmark schemes in terms of QoE improvement.Comment: 13 pages, 12 figure
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 learning-oriented 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