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Scalable Virtual Network Video-Optimizer for Adaptive Real-Time Video Transmission in 5G Networks
Abstract
The increasing popularity of video applications and ever-growing high-quality video transmissions (e.g. 4K resolutions), has encouraged other sectors to explore the growth of opportunities. In the case of health sector, mobile Health services are becoming increasingly relevant in real-time emergency video communication scenarios where a remote medical experts’ support is paramount to a successful and early disease diagnosis. To minimize the negative effects that could affect critical services in a heavily loaded network, it is essential for 5G video providers to deploy highly scalable and priorizable in-network video optimization schemes to meet the expectations of a large quantity of video treatments. This paper presents a novel 5G Video Optimizer Virtual Network Function () that leverages the latest technologies in 5G and video processing to address this important challenge. Advanced traffic filtering is coupled with Scalable H.265 video coding to enable run-time bandwidth-saving video optimization without compromising Quality of Service (QoS); kernel-space video processing is introduced to achieve further performance gains; and the use of a Virtual Network Function (VNF) facilitates dynamic deployment of virtualized video optimizers to achieve scalability and flexibility in this service. The proposed approach is implemented in a realistic 5G testbed and empirical results demonstrate the superior scalability and performance achieved.
... Centralized management architecture of 5G may produce network congestion among switches and controllers [271,272]. Hence, it is necessary to keep scalability a paramount requirement [273][274][275][276]. Similarly, a dramatic increase in UEs is a concerning factor [266]. ...
... Communication services are primarily in uenced by the mobility of devices in the wireless network. In 5G, the centralized network controller SDN can effectively address positional changes in both horizontal and vertical dimensions through sensors and geodata [266,275]. Moreover, this centralized control architecture also reduces link failures and unplanned route changes in static network devices [271,274] ...
... propose that additional adjustments should be incorporated in the protocol while others argue for developing a novel universal design from basics [266, 275,283]. [272] and [273] suggest incorporation of interoperability among various network controller software. But this concept requires in-depth analysis and further research to verify hardware and software compatibility. ...
Modern communication systems are probable to surface new challenges while introducing innovative fronts concerning context consciousness in wireless networks. The main outcome behind this expected technological jump will be a whole novel set of intuiting aptitudes forecasted for Fifth Generation (5G) enabled devices. In line with 5G, Software Defined Networking (SDN) is also rising as an intrinsically novel phenomenon. SDN is an unconventional methodology and key technology in modern communication. SDN maintains favorable novelty about network programmability, where network administration is permitted with extraordinary intellections. SDN architecture has the prospective to allow, simplify or augment security implementations in network through instantly reprogrammable centralized view of the data plane. In the near future, 5G and SDN will ripe mobile communication through the development of state-of-the-art implementations such as a smart city, advanced military security, modern national defense, intelligent traffic, etc.; thereby, these emerging mobile communication concepts invoke various significant topics, where security is a paramount implication. Therefore, we initiate our focus from basic architecture of 5G and SDN. Next, we analyze security requirements, solutions and challenges in joint paradigm of 5G and SDN. Further, considering the modern communication technological shift, we discuss future trends and research directions in the joint era of 5G and SDN technologies.
... Network slicing has now become a powerful approach to ensuring certain levels of well-performing services. Nowadays, there is a lot of research and developments in the state of the art that leverage network slicing to guarantee QoS levels among heterogeneous use cases such as the Internet of things (IoT) [17]- [19], smart grids [20], [21], smart cities [22], eHealth (e.g., telemedicine, critical services) [23], [24], or intelligent transport, education and media and entertainment [25], among others. ...
... All such metrics are directly stored in the inventory (21), shared with metric aggregator engine (22) and they are then indexed (23). Moreover, FCA makes the subscription to actions and intents (24); all intents messages received are directly stored in the inventory (25), shared with the provisioning engine (26) and then indexed (27). At this stage, slice nanager is bootstrapped and fully functional. ...
Network slicing is one of the cornerstone features of the fifth generation (5G) networks to enable the multiplexing of virtualised logical networks over the same physical network infrastructure for various vertical business services. Architectures based on network slicing enable network operators to offer end-to-end (E2E) vertical services whilst assuring fulfilling tailored service level agreement (SLA) requirements. Despite the numerous benefits that this concept offers, the need for efficient, scalable and holistic E2E management of network slicing significantly complicates network operators' network management and imposes significant challenges in the quality of service (QoS). This paper proposes a novel intent-based slice manager framework over a service base 5G architecture that allows flexible definitions to customise network slices, automates all essential management tasks for 5G network slice providers, manages the life cycles of all sorts of network slices, and guarantees their QoS in a unified network slice management framework. The proposed framework is empirically validated in a realistic large-scale 5G multi-tenant infrastructure. The high scalability of the framework is based on the empirical results supporting more than 512 physical machines, 534288 virtual machines and more than two million network interfaces.
... Conversely, insufficient demand results in supply redundancy that wastes electric energy and network resources used for pre-adjustment of video distribution. The scalability promotion of video systems with low operating costs and high quality services is crucial for sustainable development [10][11][12][13]. High-efficiency video-sharing based on accurate demand prediction can dynamically balance the supply and demand with real-time slight regulation during propagation to support energy-efficient expansion while ensuring high-quality user experiences [14][15][16]. ...
The video services that account for the majority of global network traffic consume significant amounts of electricity and network resources to meet the large-scale demand of users. Variations in user interest and social influence lead to high maintenance costs for achieving a dynamic balance between supply and demand, which negatively impacts the sustainable development of video services. In this paper, we propose a social-aware video-sharing solution using demand prediction of epidemic-based propagation in wireless networks (SDPEP). SDPEP constructs a video propagation model based on user “pull” and “push” sharing behaviors and designs an estimation method for calculating the probability of video fetching by investigating user interests and social relationships. SDPEP uses the probability of video fetching to calculate the basic reproduction number during epidemic-based video propagation, predicting user demand during the propagation process. To ensure efficient caching with low-cost adjustments to video distribution, SDPEP employs a caching-based adjustment strategy for distributing videos while maintaining dynamic balance between supply and demand. Extensive testing shows that SDPEP outperforms other state-of-the-art solutions.
... Video applications make use of the mobile internet to provide visual content to mobile users carrying handheld terminal devices, and depend on excellent technologies of wireless communication to support a high-definition and smooth viewing experience [1][2][3][4][5][6]. The target of the videos' release focuses on the large-scale popularity over the internet [7][8][9][10]. The excellent content and convenient access attract mass users, but a large number of videos have short and small-scale popularity [11][12][13]. ...
In this paper, we propose a novel video-sharing strategy based on the joint estimation of social influence and sharing capacity in wireless networks (SSISC), which promotes the scale and efficiency of video spread and ensures the balance of supply and demand. SSISC designs an estimation model of video-sharing gains by investigating social influence levels, sharing capacities (including capacities of information dispatching and video delivery), and predicted expansion scale. Some social parameters (e.g., centrality of degree and betweenness, and average shortest distance) and some parameters of sharing performance (e.g., number of forwarded messages and cached videos, the average time of transmission and freeze) are used to evaluate social influence, capacities of information dispatching, and video delivery; video interest levels, social relationship levels, and historical push success rates are used to predict video proliferation scale. A video-spreading strategy based on the assistance of spread nodes is designed, which controls the process of video push according to available bandwidth and push priority to balance supply and demand and ensure user experience quality. Extensive tests show how SSISC achieves much better performance results in comparison with other state-of-the-art solutions.
... Communication services are primarily influenced by the mobility of devices in the wireless network. In 5G, the centralized network controller SDN can effectively address positional changes in both horizontal and vertical dimensions through sensors and geodata [260]. Moreover, this centralized control architecture also reduces link failures and unplanned route changes in static network devices. ...
Modern communication systems are probable to surface new challenges while introducing innovative fronts concerning context consciousness in wireless networks. The main outcome behind this expected technological jump will be a whole novel set of intuiting aptitudes forecasted for Fifth Generation (5G) enabled devices. In line with 5G, Software Defined Networking (SDN) is also rising as an intrinsically novel phenomenon. SDN is an unconventional methodology and key technology in modern communication. SDN maintains favorable novelty about network programmability, where network administration is permitted with extraordinary intellections. SDN architecture has the prospective to allow, simplify or augment security implementations in network through instantly reprogrammable centralized view of the data plane. In the near future, 5G and SDN will ripe mobile communication through the development of state-of-the-art implementations such as a smart city, advanced military security, modern national defense, intelligent traffic, etc.; thereby, these emerging mobile communication concepts invoke various significant topics, where security is a paramount implication. Therefore, we initiate our focus from basic architecture of 5G and SDN. Next, we analyze security requirements, solutions and challenges in joint paradigm of 5G and SDN. Further, considering the modern communication technological shift, we discuss future trends and research directions in the joint era of 5G and SDN technologies.
... Since for best selection of the RAT provides the better connectivity of each user via the best serving RAT network. The author has introduced the RAT selection mechanism where the user selects the best RAT service based on the user and network-based parameters [2]. Video encoding is one of the significant processing the transmission of video in the 5G network. ...
... Through the literature review, there are manifold studies that approach software-level solutions to address this topic. In [11], an efficient virtual videooptimization mechanism which maintains QoS in critical services is proposed. This paper uses iptables to control and optimise video traffic in multi-tenant 5G networks. ...
Next-generation networks are expected to combine advanced physical and digital technologies in super-high-speed connected system infrastructures, gaining critical operation competitiveness of improved efficiency, productivity and quality of services. Towards a fully digital and connected world, these platforms will enable infrastructure virtualization and support of edge processing, making emerging sectors, such as Industry 4.0, ready to exploit its full potentials. Nevertheless, the fast growth of data-centric and automated systems may exceed the capabilities of the overall infrastructure beyond the radio access networks, becoming unable to fulfil the demands of vertical sectors and representing a bottleneck. To minimize the negative effects that could affect critical services in a heavily loaded network, it is essential for network providers to deploy highly scalable and prioritisable in-network optimisation schemes to meet industry expectations in next-generation networks. To this end, this work presents a novel framework that leverages extended Berkeley Packet Filter (eBPF) and eXpress Data Path (XDP) to offload network functions to reduce unnecessary overhead in the backbone infrastructure. The proposed solution is envisioned to be implemented as a Network Application (NetApp) service, which will greatly benefit the compatibility with next-generation networking ecosystem empowered by Artificial Intelligence (AI), advanced automation, multi-domain network slicing, and other related technologies. The achieved results demonstrate key performance improvements in terms of packet processing capacity as high as about 18 million packets per second (Mpps), system throughput up to 6.1 Mpps with 0% of packet loss, and illustrate the flexibility of the framework to adapt to multiple network policy rules dynamically on demand.
... Patil et al. [6] described the 5G technology, which will meet all of the demands of users who need sophisticated capabilities in their mobile phones. A visual data communication protocol has been developed for the real-time broadcasting of UAV video in [7]. is protocol can reduce the amount of time it takes to record a video and send it to the client's display. ...
The advent of 5G technology has brought ease to many sectors of society and lifestyle. 5G technology has also made significant contributions to education, with a significant positive influence on physical education, especially for online learning. This study examined the online basketball learning system based on the 5G mobile communication technology as well as traditional face-to-face teaching and online learning and analyzed the students’ performance using different evaluation techniques. The network latency is minimized by detecting and correcting the main energy-consuming activities of the 5G network to deliver effective online training to students utilizing 5G basketball teaching (5G_BBT). The features of 5G network nodes and channel characteristics are used to build a comprehensive model of basketball training. The information that could be sent to the user protocol is obtained from the teaching database, and the cluster formation is processed using crossover swarm optimization based on passive clustering. The maximum-likelihood naïve Bayes function is employed to compute the transmission of basketball data transmission, and the border gateway algorithm is used to calculate the optimum cluster path for transmitting the training material, based on distance and residual energy constraints. The model is evaluated in terms of latency energy efficiency and speed to prove the effectiveness of the proposed 5G networking scheme. The proposed algorithm achieved a high network speed of 98%, data transfer efficiency of 90%, and time complexity of 32%, respectively. The 5G-driven basketball training approach will provide students with objective, fair, and diversified learning and adaptive learning services to promote the overall development of students.
... Applications of this type pose increasingly stringent requirements with an emphasis on low latency and reliability. Examples of such applications are industrial control [3], remote diagnosis [4] and surgery [5,6], online gaming [7], video conferencing [8], autonomous driving [9], as well as Virtual Reality (VR) and Augmented Reality (AR) systems [10]. The epitome of requirements for such applications is the Ultra-Reliable Low-Latency Communication (URLLC) specification [11,12]. ...
Sliding Window RLNC schemes represent a novel, promising and efficient class of algorithms for reliable data transmission over an unreliable link. They combine Random Linear Network Coding (RLNC) with the idea of sliding window from (Automatic Repeat reQuest) ARQ protocols in order to help the basic ARQ mechanism to quickly recover from losses through redundant coded packets. Although very successful, we make the observation that such schemes bear significant limitations stemming from the fact that both the ARQ mechanism and the coding process operate on the same group of packets, called the sliding window. To tackle the problem, we propose the use of two distinct windows; the sliding window, which can be used to optimize the data flow based on the link’s bandwidth-delay product, and the coding window that can be used exclusively in the coding process. We analyze the performance of the proposed strategy and show that it provides an improved coding operation while at the same time reduces the coding complexity. Then, we delineate rapidARQ, a sliding window RLNC protocol that adopts the proposed strategy, and experimentally confirm that it outperforms other state-of-the-art sliding window RLNC schemes. More specifically, it achieves superior throughput-delay performance that better fits in the context of Ultra-Reliable Low-Latency Communication (URLLC) while at the same time it significantly reduces the coding complexity. Moreover, we show that the superior performance of rapidARQ is more prominent in channels with large bandwidth-delay products, a fact that renders its utility to current and future networks more essential.
... Network slicing has now become a key technology to ensure certain levels of well-performing services. Nowadays, there are multiple research and developments in the state of the art leveraging network slicing to guarantee QoS levels among heterogeneous use cases such as the Internet of Things (IoT) [9], Smart Grids [10], Smart Cities [11], eHealth [12], or Intelligent Transport, Education and Media and Entertainment [13], among others. ...
... In the sender-driven method [18], receivers signal the device capabilities while creating the session; then, accordingly, the sender adapts the content and streams the adapted content. A Video Optimizer Virtual Network Function [19] has been proposed to implement dynamic video optimization, where a video processing module at kernel and Network function virtualization (NFV) are used to improve the quality of the video in 5G network. There are many work on Hypertext Transfer Protocol (HTTP)based dynamic video adaptation methods [20][21][22][23][24] that use server-driven method. ...
A wireless network gives flexibility to the user in terms of mobility that attracts the user to use wireless communication more. The video communication in the wireless network experiences Quality of Services (QoS) and Quality of Experience (QoE) issues due to network dynamics. The parameters, such as node mobility, routing protocols, and distance between the nodes, play a major role in the quality of video communication. Scalable Video Coding (SVC) is an extension to H.264 Advanced Video Coding (AVC), allows partial removal of layers, and generates a valid adapted bit-stream. This adaptation feature enables the streaming of video data over a wireless network to meet the availability of the resources. The video adaptation is a dynamic process and requires prior knowledge to decide the adaptation parameter for extraction of the video levels. This research work aims at building the adaptation parameters that are required by the adaptation engines, such as Media Aware Network Elements (MANE), to perform adaptation on-the-fly. The prior knowledge improves the performances of the adaptation engines and gives the improved quality of the video communication. The unique feature of this work is that, here, we used an experimental evaluation method to identify the video levels that are suitable for a given network condition. In this paper, we estimated the adaptation parameters for streaming scalable video over the wireless network using the experimental method. The adaptation parameters are derived using node mobility, link bandwidth, and motion level of video sequences as deciding parameters. The experimentation is carried on the OMNeT++ tool, and Joint Scalable Video Module (JSVM) is used to encode and decode the scalable video data.
Media services must ensure an enhanced user’s perceived quality during content playback to attract and retain audiences, especially while the streams are distributed remotely via networks. Thus, media streaming services rely heavily on good and predictable network performance when delivered to a large number of people. Furthermore, as the quality of media content gets high, the network performance demands are also increasing, and meeting them is challenging. Network functions devoted to improving media streaming services become essential to cope with the high dynamics of network performance and user mobility. Furthermore, new networking paradigms and architectures under the 5G networks umbrella are bringing new possibilities to deploy smart network functions, which monitor the media streaming services through live and objective metrics and boost them in real-time. This survey overviews the state-of-the-art technologies and solutions proposed to apply new network functions for enhancing media streaming.
Wireless spectrum environment is complex and uncertain in practical scenarios. For spectrum resource management, there are two crucial challenges to be resolved. First, channel states are always time-varying, thus violent fluctuations of interference links bring difficulties to interference mitigation. Second, the optimization efficiency degrades when jointly considering frequency coordination and power adaption. In this paper, we focus on the interference-aware resource management in a dynamic radio environment. First, we propose a new metric named effective weighted aggregate interference to capture the fluctuations of time-varying interference links. Then, we investigate the joint channel selection and power assignment problem from the perspective of game theoretic, and propose a novel mechanism called action freezing to significantly improve the optimization efficiency. Furthermore, we develop a higher-order statistic optimization based multi-agent strategic learning (HSOMSL) algorithm to alleviate the effect of fluctuating payoffs and obtain stable solutions. Simulation results illustrate that the proposed algorithm has lower experienced interference, higher achievable throughput, and better energy efficiency compared to existing state-of-the-art algorithms. In addition, it is verified to be effective in mobile scenarios.
The evolution of wireless network services has enabled consumers and intelligent devices to freely exchange information with each other. Mobile users frequently exchange popular contents, resulting in massive increase in the mobile traffic. The redundant mobile traffic can be reduced by archiving the frequently accessed data within a 5G core network or radio access network, and demands for the same content can be readily met without relying on remote servers. In this paper, we propose an eNB/gNB aware data retrieval algorithm along with Liveliness and Size based data Replacement algorithm to refine, rank, and cache the data items efficiently. Data items are selected based on their popularity and cached in D-RAN for efficient data replacement. We have also included a cost-optimized Radar-Based data Retrieval algorithm that helps to find the data nearness in the neighbouring eNBs. In our proposed technique, unique contents are maintained at each end of the cluster to aid in extending content diversity within the cluster. The experimental analysis shows that the proposed model achieves lower latency, lower congestion, and higher cache hit ratio in 5G networks.
WebRTC has quickly become popular as a video conferencing platform, partly due to the fact that many browsers support it. WebRTC utilizes the Google Congestion Control (GCC) algorithm to provide congestion control for realtime communications over UDP. The performance during a WebRTC call may be influenced by several factors, including the underlyingWebRTC implementation, the device and network characteristics, and the network topology. In this paper, we perform a thorough performance evaluation of WebRTC both in emulated synthetic network conditions as well as in real wired and wireless networks. Our evaluation shows that WebRTC streams have a slightly higher priority than TCP flows when competing with cross traffic. In general, while in several of the considered scenarios WebRTC performed as expected, we observed important cases where there is room for improvement. These include the wireless domain and the newly added support for the video codecs VP9 and H.264 that does not perform as expected.
This memo describes an RTP payload format for the video coding standard ITU-T Recommendation H.265 and ISO/IEC International Standard 23008-2, both also known as High Efficiency Video Coding (HEVC) and developed by the Joint Collaborative Team on Video Coding (JCT-VC). The RTP payload format allows for packetization of one or more Network Abstraction Layer (NAL) units in each RTP packet payload as well as fragmentation of a NAL unit into multiple RTP packets. Furthermore, it supports transmission of an HEVC bitstream over a single stream as well as multiple RTP streams. When multiple RTP streams are used, a single transport or multiple transports may be utilized. The payload format has wide applicability in videoconferencing, Internet video streaming, and high-bitrate entertainment-quality video, among others.
Recently, dynamic adaptive streaming over HTTP (DASH) has been widely deployed on the Internet. However, the research about DASH over multiple content distribution servers (MCDS-DASH) is limited. Compared with traditional single-server DASH, MCDS-DASH is able to offer expanded bandwidth, link diversity, and reliability. It is, however, a challenging problem to smooth video bitrate switching over multiple servers due to their diverse bandwidths. In this paper, we propose a block-based rate adaptation method considering both the diverse bandwidths and feedback buffered video time. In our method, multiple fragments are grouped into a block and the fragments are downloaded in parallel from multiple servers. We propose to adapt video bitrate at the block level rather than at the fragment level. By dynamically adjusting the block length and scheduling fragment requests to multiple servers, the requested video bitrates from the multiple servers are synchronized, making the fragments download in an orderly way. Then, we propose a control-theoretic approach to select an appropriate bitrate for each block. By modeling and linearizing the rate adaption system, we propose a novel proportional-derivative controller to adapt video bitrate with high responsiveness and stability. Theoretical analysis and extensive experiments on our network testbed and the Internet demonstrate the good efficiency of the proposed method.
With the confluence of the growing market for mobile Internet devices, and users' expectations of instant access to high-quality multimedia content, the delivery of video over wireless networks has become the challenge of the decade. Dynamic Adaptive Streaming over HTTP (DASH) and WebRTC are new and evolving standards that have been developed specifically to meet this demand and enable a high-quality experience for mobile users of video on demand and real time communication services, respectively. However, there has been no systematic study of how these services are experienced by users in a realistic mobile setting. In this work, we describe measurements collected from DASH and WebRTC implementations while moving at walking speeds through an 802.16e WiMAX network. Using data from the application, network, and physical layers, in different wireless environments, we identify characteristics of the cellular data network that directly impact the quality of video service, and suggest areas for further improvement.
HTTP Adaptive Streaming (HAS) is becoming the de facto standard for Over-The-Top (OTT)-based video streaming services such as YouTube and Netflix. By splitting a video into multiple segments of a couple of seconds and encoding each of these at multiple quality levels, HAS allows a video client to dynamically adapt the requested quality during the playout to react to network changes. However, state-of-the-art quality selection heuristics are deterministic and tailored to specific network configurations. Therefore, they are unable to cope with a vast range of highly dynamic network settings. In this letter, a novel Reinforcement Learning (RL)-based HAS client is presented and evaluated. The self-learning HAS client dynamically adapts its behaviour by interacting with the environment to optimize the Quality of Experience (QoE), the quality as perceived by the end-user. The proposed client has been thoroughly evaluated using a network-based simulator and is shown to outperform traditional HAS clients by up to 13% in a mobile network environment.
We present a new architecture to handle the ongoing explosive increase in the
demand for video content in wireless networks. It is based on distributed
caching of the content in femto-basestations with small or non-existing
backhaul capacity but with considerable storage space, called helper nodes. We
also consider using the mobile terminals themselves as caching helpers, which
can distribute video through device-to-device communications. This approach
allows an improvement in the video throughput without deployment of any
additional infrastructure. The new architecture can improve video throughput by
one to two orders-of-magnitude.
The scalable video coding (SVC) standard as an extension of H.264/AVC allows efficient, standard-based temporal, spatial, and quality scalability of video bit streams. Scalability of a video bit stream allows for media bit rate as well as for device capability adaptation. Moreover, adaptation of the bit rate of a video signal is a desirable key feature, if limitation in network resources, mostly characterized by throughput variations, varying delay or transmission errors, need to be considered. Typically, in mobile networks the throughput, delay and errors of a connection (link) depend on the current reception conditions, which are largely influenced by a number of physical factors. In order to cope with the typically varying characteristics of mobile communication channels in unicast, multicast, or broadcast services, different methods for increasing robustness and achieving quality of service are desirable. We will give an overview of SVC and its relation to mobile delivery methods. Furthermore, innovative use cases are introduced which apply SVC in mobile networks.
While cloud servers provide a tremendous amount of resources for networked video applications, most successful stories of cloud-assisted video applications are presentational video services, such as YouTube and NetFlix. This article surveys the recent advances on delay-sensitive video computations in the cloud, which are crucial to cloud-assisted conversational video services, such as cloud gaming, Virtual Reality (VR), Augmented Reality (AR), and telepresence. Supporting conversational video services with cloud resources is challenging because most cloud servers are far away from the end users while these services incur the following stringent requirements: high bandwidth, short delay, and high heterogeneity. In this article, we cover the literature with a top-down approach: from applications and experience, to architecture and management, and to optimization in and outside of the cloud. We also point out major open challenges, hoping to stimulate more research activities in this emerging and exciting direction.
Distributed caching architectures have been proposed for bringing content close to requesters, and the key problem is to design caching algorithms for reducing content delivery delay, which determines to an extent the user Quality of Experience (QoE). This problem obtains an interesting new twist with the advent of advanced layered-video encoding techniques such as Scalable Video Coding. In this paper, we show that the problem of finding the caching configuration of video encoding layers that minimizes delivery delay for a network operator is NP-Hard, and we establish a pseudopolynomial-time optimal solution by using a connection with the multiple-choice knapsack problem. Next, we design caching algorithms for multiple network operators that cooperate by pooling together their co-located caches, in an effort to aid each other, so as to avoid large delays due to fetching content from distant servers. We derive an approximate solution to this cooperative caching problem by using a technique that partitions the cache capacity into amounts dedicated to own and other operators' caching needs. Trace-driven evaluations demonstrate up to 25% reduction in delay over existing caching schemes. As a side benefit, our algorithms achieve smoother playback for video streaming applications, with fewer playback stalls and higher decoded quality.
Traffic on future fifth-generation (5G) mobile networks is predicted to be dominated by challenging video applications such as mobile broadcasting, remote surgery and augmented reality, demanding real-time, and ultra-high quality delivery. Two of the main expectations of 5G networks are that they will be able to handle ultra-high-definition (UHD) video streaming and that they will deliver services that meet the requirements of the end user's perceived quality by adopting quality of experience (QoE) aware network management approaches. This paper proposes a 5G-QoE framework to address the QoE modeling for UHD video flows in 5G networks. Particularly, it focuses on providing a QoE prediction model that is both sufficiently accurate and of low enough complexity to be employed as a continuous real-time indicator of the "health" of video application flows at the scale required in future 5G networks. The model has been developed and implemented as part of the EU 5G PPP SELFNET autonomic management framework, where it provides a primary indicator of the likely perceptual quality of UHD video application flows traversing a realistic multi-tenanted 5G mobile edge network testbed. The proposed 5G-QoE framework has been implemented in the 5G testbed, and the high accuracy of QoE prediction has been validated through comparing the predicted QoE values with not only subjective testing results but also empirical measurements in the testbed. As such, 5G-QoE would enable a holistic video flow self-optimisation system employing the cutting-edge Scalable H.265 video encoding to transmit UHD video applications in a QoE-aware manner. IEEE
Ultra-High-Definition (UHD) video applications such as streaming are envisioned as a main driver for the emerging Fifth Generation (5G) mobile networks being developed worldwide. This paper focuses on addressing a major technical challenge in meeting UHD users' growing expectation for continuous high-quality video delivery in 5G hotspots where congestion is commonplace to occur. A novel 5G-UHD framework is proposed towards achieving adaptive video streaming in this demanding scenario to pave the way for self-optimisation oriented 5G UHD streaming. The architectural design and the video stream optimisation mechanism are described, and the system is prototyped based on a realistic virtualised 5G testbed. Empirical experiments validate the design of the framework and yield a set of insightful performance evaluation results.
DASH, the Dynamic adaptive video streaming over hypertext transfer protocol (HTTP), has become the de-facto video delivery mechanism nowadays, which takes advantage of the existing low cost and wide-spread HTTP platforms. Standards like MPEG-DASH defines the bitstreams conformance and decoding process, while leaving the bitrate adaptive algorithm open for research. So far, most DASH researches focus on the constant bitrate video delivery. In this paper, various bitrate (VBR) video delivery is investigated in the on-demand streaming scenario. Detailed instant bitrates of future segments are exploited in the proposed adaptation method to grasp the fluctuation traits of the VBR video. Meanwhile, the adaptation problem is formulated as an optimization process with the proposed internal QoE goal function, which keeps a good balance between various requirements. Besides, the parameters within the internal QoE function can be tuned to guarantee the flexibility of meeting different preferences. The experimental results demonstrate that our proposed QoE-based video adaptation method outperforms the state-of-the-art method with a good margin.
With ever increasing growth in the number of users accessing online videos through mobile phones and tablets, the online video traffic is also growing. In today's network environment, when a mobile user requests for online videos, it should be fetched from Content Delivery Network servers. The video has to pass through several gateways in the core network and base station in Radio Access Network before reaching user's equipment. This adds to video latency and sometimes request may be denied due to congestion in the wireless network backhaul. In this paper, a video popularity based cooperative edge caching alongwith a proactive cache updating policy is proposed with an aim to reduce the delay. Simulation of the proposed technique is performed using MATLAB and the results show that our co-operative caching scheme can improve the cache hit ratio by 13 percentage points compared to conventional cache replacement scheme LRU and by 2 percentage compared to edge caching without cooperation.
Today's Internet has witnessed an increase in the popularity of mobile video streaming, which is expected to exceed 3/4 of the global mobile data traffic by 2019. To satisfy the considerable amount of mobile video requests, video service providers have been pushing their content delivery infrastructure to edge networks--from regional CDN servers to peer CDN servers (e.g., smartrouters in users' homes)--to cache content and serve users with storage and network resources nearby. Among the edge network content caching paradigms, Wi-Fi access point caching and cellular base station caching have become two mainstream solutions. Thus, understanding the effectiveness and performance of these solutions for large-scale mobile video delivery is important. However, the characteristics and request patterns of mobile video streaming are unclear in practical wireless network. In this paper, we use real-world datasets containing 50 million trace items of nearly 2 million users viewing more than 0.3 million unique videos using mobile devices in a metropolis in China over 2 weeks, not only to understand the request patterns and user behaviors in mobile video streaming, but also to evaluate the effectiveness of Wi-Fi and cellular-based edge content caching solutions. To understand performance of edge content caching for mobile video streaming, we first present temporal and spatial video request patterns, and we analyze their impacts on caching performance using frequency-domain and entropy analysis approaches. We then study the behaviors of mobile video users, including their mobility and geographical migration behaviors. Using trace-driven experiments, we compare strategies for edge content caching including LRU and LFU, in terms of supporting mobile video requests. Moreover, we design an efficient caching strategy based on the measurement insights and experimentally evaluate its performance.
With a new video coding standard high efficiency video coding (HEVC), the ultra high definition (UHD) TV service with robust video streaming technology is emerging in the TV industry. This paper addresses the system architecture for the UHD video streaming and proposes two main ideas: (i) picture prioritization method, (ii) error concealment mode signaling (ECMS). In the experiments using HEVC reference model conducted, the proposed picture prioritization method shows the gains in video quality from 2.2 to 7.5 dB in Y-PSNR, and the error concealment mode signaling gains from 0.2 to 2.5 dB in Y-PSNR, with corresponding subjective improvements.
M-health services are expected to become increasingly relevant in the management of emergency situations by enabling real-time support of remote medical experts. In this context, the transmission of multiple health-related video streams from an ambulance to a remote hospital can improve the efficacy of the teleconsultation service, but requires a large bandwidth to meet the desired quality, not always guaranteed by the mobile network. In order to deliver the multiple streams over a single bandwidth-limited wireless access channel, in this paper we propose a novel optimization framework that enables to classify the available video sources and to automatically select and adapt the best streams to transmit. The camera ranking algorithm jointly works with a cross-layer adaptation strategy for multiple scalable streams to achieve different objectives and/or tradeoffs in terms of number and target quality of the transmitted videos. The final goal of the optimization is to dynamically adjust the overall transmitted throughput to meet the actual available bandwidth, while being able to provide high quality to diagnostic video sequences and lower quality to less critical ambient videos. Numerical simulations considering a realistic emergency scenario with long term evolution advanced (LTE-A) connectivity show that the proposed content/context-aware solution is able to automatically select the best sources of information from a visual point of view and to achieve optimal end-to-end video quality for both the diagnostic and the ambient videos.
An HEVC bitstream consists of a sequence of data units called network abstraction layer (NAL) units. Some NAL units contain parameter sets that carry high-level information regarding the entire coded video sequence or a subset of the pictures within it. Other NAL units carry coded samples in the form of slices that belong to one of the various picture types that are defined in HEVC. Some picture types indicate that the picture can be discarded without affecting the decodability of other pictures, and other picture types indicate positions in the bitstream where random access is possible. The slices contain information on how decoded pictures are managed, both what previous pictures to keep and in which order they are to be output. Some NAL units contain optional supplementary enhancement information (SEI) that aids the decoding process or may assist in other ways, such as providing hints about how best to display the video. The syntax elements that describe the structure of the bitstream or provide information that applies to multiple pictures or to multiple coded block regions within a picture, such as the parameter sets, reference picture management syntax, and SEI messages, are known as the “high-level syntax” part of HEVC. A considerable amount of attention has been devoted to the design of the high-level syntax in HEVC, in order to make it broadly applicable, flexible, robust to data losses, and generally highly capable of providing useful information to decoders and receiving systems.
To meet the challenging key performance indicators of the fifth generation (5G) system, the network
infrastructure becomes more heterogeneous and complex. This will bring a high pressure on the
reduction of OPEX and the improvement of the user experience. Hence, shifting today’s manual and
semi-automatic network management into an autonomic and intelligent framework will play a vital role in the upcoming 5G system. Based on the cutting-edge technologies, such as Software-Defined Networking and Network Function Virtualization, a novel management framework upon the software defined and virtualized network is proposed by EU H2020 SELFNET project. In the paper, the reference architecture of SELFNET, which is divided into Infrastructure layer, Virtualized Network layer, SON Control layer, SON Autonomic layer, NFV Orchestration and Management layer and Access layer, will be presented.
This paper provides an overview of Scalable High efficiency Video Coding (SHVC), the scalable extensions of the High Efficiency Video Coding (HEVC) standard, published in the second version of HEVC. In addition to the temporal scalability already provided by the first version of HEVC, SHVC further provides spatial, signal-to-noise ratio, bit depth, and color gamut scalability functionalities, as well as combinations of any of these. The SHVC architecture design enables SHVC implementations to be built using multiple repurposed single-layer HEVC codec cores, with the addition of interlayer reference picture processing modules. The general multilayer high-level syntax design common to all multilayer HEVC extensions, including SHVC, MV-HEVC, and 3D HEVC, is described. The interlayer reference picture processing modules, including texture and motion resampling and color mapping, are also described. Performance comparisons are provided for SHVC versus simulcast HEVC and versus the scalable video coding extension to H.264/advanced video coding.
In this paper, we introduce distributed caching of videos at the base stations of the Radio Access Network (RAN) to significantly improve the video capacity and user experience of mobile networks. To ensure effectiveness of the massively distributed but relatively small-sized RAN caches, unlike Internet content delivery networks (CDNs) that can store millions of videos in a relatively few large-sized caches, we propose RAN-aware reactive and proactive caching policies that utilize User Preference Profiles (UPPs) of active users in a cell. Furthermore, we propose video-aware backhaul and wireless channel scheduling techniques that, in conjunction with edge caching, ensure maximizing the number of concurrent video sessions that can be supported by the end-to-end network while satisfying their initial delay requirements and minimize stalling. To evaluate our proposed techniques, we developed a statistical simulation framework using MATLAB and performed extensive simulations under various cache sizes, video popularity and UPP distributions, user dynamics, and wireless channel conditions. Our simulation results show that RAN caches using UPP-based caching policies, together with video-aware backhaul scheduling, can improve capacity by 300% compared to having no RAN caches, and by more than 50% compared to RAN caches using conventional caching policies. The results also demonstrate that using UPP-based RAN caches can significantly improve the probability that video requests experience low initial delays. In networks where the wireless channel bandwidth may be constrained, application of our video-aware wireless channel scheduler results in significantly (up to 250%) higher video capacity with very low stalling probability.
https://tools.ietf.org/html/rfc6184
This paper focuses on network delays as they apply to voice traffic. First the nature of the delay problem is discussed and this is followed by a review of enhanced circuit, packet, and hybrid switching techniques: these include fast circuit switching (FCS), virtual circuit switching (VCS), buffered speech interpolation (SI), packetized virtual circuit (PVC), cut-through switching (CTS), composite packets, and various frame management strategies for hybrid switching. In particular, the concept of introducing delay to resolve contention in SI is emphasized, and when applied to both voice talkspurts and data messages, forms a basis for a relatively new approach to network design called transparent message switching (TMS). This approach and its potential performance advantages are reviewed in terms of packet structure, multiplexing scheme, network topology, and network protocols. The paper then deals more specifically with the impact of variable delays on voice traffic. In this regard the importance of generating and preserving appropriate length speech talkspurts in order to mitigate the effects of variable network delay is emphasized. The results indicate that a desirable length of talkspurt "hangover" of about 200 ms will accomplish this without unduly affecting speech activity, and that, under these circumstances, the perceptable threshold of variable talkspurt delay can be as high as about 200 ms average. As such, the results provide a useful guideline for integrated services system designers. Finally, suggestions are made for further studies on performance analysis and subjective evaluation of advanced integrated services systems.
Many versions of Unix provide facilities for user-level packetcapture, making possible the use of general purpose workstationsfor network monitoring. Because network monitorsrun as user-level processes, packets must be copied across thekernel/user-space protection boundary. This copying can beminimized by deploying a kernel agent called a packet filter,which discards unwanted packets as early as possible. Theoriginal Unix packet filter was designed around a stack-basedfilter evaluator...
This document specifies the incorporation of ECN (Explicit Congestion Notification) to TCP and IP, including ECN's use of two bits in the IP header. We begin by describing TCP's use of packet drops as an indication of congestion. Next we explain that with the addition of active queue management (e.g., RED) to the Internet infrastructure, where routers detect congestion before the queue overflows, routers are no longer limited to packet drops as an indication of congestion. Routers can instead set the Congestion Experienced (CE) codepoint in the IP header of packets from ECN-capable transports. We describe when the CE codepoint is to be set in routers, and describe Ramakrishnan, Floyd, Black Proposed Standard [Page 1] draft-ietf-tsvwg-ecn-04 Addition of ECN to IP June 2001 modifications needed to TCP to make it ECN-capable. Modifications to other transport protocols (e.g., unreliable unicast or multicast, reliable multicast, other reliable unicast transport protocols) could be considered as those protocols are developed and advance through the standards process. We also describe in this document the issues involving the use of ECN within IP tunnels, and within IPsec tunnels in particular. One of the guiding principles for this document is that, to the extent possible, the mechanisms specified here be incrementally deployable. One challenge to the principle of incremental deployment has been the prior existence of some IP tunnels that were not compatible with the use of ECN. As ECN becomes deployed, noncompatible IP tunnels will have to be upgraded to conform to this document. This document is intended to obsolete RFC 2481, "A Proposal to add Explicit Congestion Notification (ECN) to IP", which defined ECN as an Experimental Protocol for the Internet Communit...
WebRTC 1.0: Real-time communication between browsers
- A Bergkvist
- D Burnett
- C Jennings
- A Narayanan