Video Streaming Applications Research Articles

WDM-Based 160 Gbps Radio Over Fiber System With the Application of Dispersion Compensation Fiber and Fiber Bragg Grating

The demand for data transmission is rising expensively for the applications of biomedical sensors data, multimedia technologies, and ultrahigh-definition online video streaming. Such applications require larger bandwidth with minimum latency and seamless service delivery. Radio-over-fiber (RoF), integrated with wavelength division multiplexing (WDM) technology, is being considered one of the promising technologies. However, the integration of optical fiber and wireless communication also generates non-linear effects as and when the number of users increases. That results in the introduction of signal noise, unwanted frequencies, low quality of signals, and increased latency. In this paper, a 16-channel 160 Gbps data rate WDM-based RoF system has been simulated and evaluated for optimum performance at a variable input power level, from 5 to −15 dBm, with the application of dispersion compensation fiber (DCF) and fiber Bragg grating (FBG), with channel spacing of 50 and 100 GHz. The performance of the system is evaluated with the existing WDM-RoF system. The performance metrics parameters chosen for evaluation are bit error rate (BER), quality factor (Q-factor), and eye diagrams and simulated on opti-system simulator. The optimum performance has been observed at a power level of −5 dBm for all these elected evaluation parameters. It has also been observed that, for channel spacing of 100 GHz, the network performed better in comparison with 50 GHz.

Scheduling Inter-Datacenter Video Flows for Cost Efficiency

As video streaming applications are deployed on the cloud, cloud providers are charged by ISPs for inter-datacenter transfers under the dominant percentile-based charging models. In order to minimize the payment costs, existing works aim to keep the traffic on each link under the charging volume. However, these methods cannot fully utilize each link's available bandwidth capacity. As a solution, we propose an economical and deadline-driven video flow scheduling system, called EcoFlow. Considering that different video flows have different transmission deadlines, EcoFlow transmits videos in the order of their deadline tightness and postpones the deliveries of later-deadline videos to later time slots. The flows that are expected to miss their deadlines are divided into subflows to be rerouted to other under-utilized links. We also propose setting each link's initial charging volume to reduce the scheduling latency at the beginning of the charging period and discuss how to deal with issues such as the prediction errors of link available bandwidth and the lack of charging volume's prior knowledge. Furthermore, we designed implementation strategies for using EcoFlow in both centralized and distributed situations. Experimental results demonstrate that EcoFlow achieves lower bandwidth costs and higher video flow transmission rates when compared to existing methods.

Genre Classification of Telugu and English Movie Based on the Hierarchical Attention Neural Network

Genre Classification of movies is useful in the movie recommendation system for video streaming applications like Amazon, Netflix, etc. The existing methods used either video or audio data as input that requires more computation resources to process the data for the genre classification of movies. In this study, the Hierarchical Attention Neural Network (HANN) is proposed for genre classification of movies based on the social media called Twitter data as input. Twitter data related to the Telugu and English movies are collected and applied to HANN for movie’s genre classification. IMDB data are used to evaluate the performance of the proposed HANN method. The hierarchical structures of the twitter data is considered by the proposed HANN method and the most important words related to genre classification is identified by the attention mechanism, where the other neural networks such as Artificial Neural Network and Convolutional Neural Network (CNN) returns only the important weights resulting from previous words. The HANN method has the advantages of encoding the relevant information that helps to improve the performance of the recommendation system. The experimental results show that the HANN method achieve higher performance compared to other classifiers Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM). The HANN method achieves accuracy of 73.15% in classification, while the existing BiLSTM method achieve the accuracy of 68% in classification.

A novel QoE based cross-layer scheduling scheme for video applications in 802.11 wireless LANs

In the past two decades, 802.11 based Wireless Local Area Networks (WLANs) have gained popularity in offering Internet services and increasingly supporting data driven video streaming applications. Such services demand enhanced view quality to all the end-users in the network. However, due to the sharing of limited resources and lack of proper scheduling mechanisms, WLANs are unable to meet the requirements of numerous video users and provide them better quality. Therefore, an effective resource scheduling approach is needed that can achieve high user satisfaction for the ever changing user needs for quality video viewing. In this paper, a Quality of Experience based Cross-Layer Scheduling (QoECLS) scheme to enhance the user experience for video applications in WLANs is proposed. Here, the Mean Opinion Score has been used as a measure to evaluate the user experience. In order to provide the average user throughput with guarantee of fairness among users, the QoECLS scheme uses cross-layer information from the application and physical layer, respectively. The QoECLS algorithm allocates transmission resources by considering application feedback along with channel state information and buffer status. The performance of the QoECLS approach was extensively studied through simulations and the results showed an improvement in user experience and throughput, while maintaining fairness among the users. In terms of throughput, the proposed scheme achieves 25%, 45 %, and 50% improvement compared to QoE Aware Scheduling scheme, Modified-Largest Weighted Delay First scheduler and Exponential-Proportional Fair scheduler, respectively.

Cross-layer DASH-based multipath video streaming over LTE and 802.11ac networks

Dynamic Adaptive Streaming over Http (DASH) -based video streaming applications are becoming increasingly prevalent over the mobile Internet. Many efforts have been made to optimize their performances. Multipath video streaming that simultaneously utilizes multiple wireless networks for video content delivery is a common method. Another effective approach is the cross-layer video streaming optimization that jointly takes the parameters at different protocol layers into account. However, multipath streaming schemes mainly focus on how to efficiently utilize multiple wireless networks and the collaboration of parameters at different layers in each network is neglected. Likewise, the cross-layer schemes normally optimize the parameters at different layers in purely one network without fully utilizing the aggregated bandwidths of multiple available wireless networks. Therefore, both of them are sub-optimal and might suffer from degrading performance. In this paper, we propose a joint Cross-layer DASH-based multipath video streaming scheme that takes advantage of bandwidth aggregation of multiple wireless networks and further improves the performance by optimizing the different layers’ parameters in each network with a cross-layer manner. In the proposed scheme, the LTE and 802.11ac networks are adopted. The bitrate of DASH-based video chunk at application layer, the rate allocation among networks and the Modulation and Coding Scheme (MCS) at physical layers in LTE and 802.11ac downlink are jointly optimized. We also compare our proposed scheme to state-of-the-art schemes using trace-driven experiments. Experimental results show that our proposed scheme outperforms state-of-the-art schemes in terms of PSNR, normalized QoE, and balance between video bitrate and rebuffering penalty.

Exploiting user preferences to reduce bandwidth requirements for VoD services with client caching

Internet traffic is already dominated by video streaming applications. The quality of transmitted videos and the number of content available online is expected to increase in the upcoming years, which will further pressure the available network infrastructures. In this scenario, Video-on-Demand services require that distribution mechanisms improve the efficiency of video transmission, which impacts the network performance and system scalability. The efficiency of distribution is strongly related to the operating costs of the providers of such services. The most efficient methods available for Video-on-Demand distribution use strategies that combine multicast transmission and the storage capacity of the user's equipment. This paper presents a new method for Video-on-Demand distribution that explores the client storage capabilities by modelling the users' preferences with a Hidden Markov Model. The efficiency of the proposed method is demonstrated through computational simulations of different scenarios, including a sample of real users' activity. Our results indicate that the proposed scheme substantially outperforms the current Video-on-Demand distribution mechanisms in terms of network bandwidth consumption, significantly reducing operation costs by improving the system scalability.

Low Quality for High Quality: Exploiting 2K Frames for Supporting Efficient 4K-Quality Pervasive Video Streaming Applications

Emerging cloud-based video streaming applications suggest limitations on current video streaming systems and emphasize the need to preserve and exchange high-quality contents while minimizing processing latency, given that both are critical in perpetuating a high user-perceived quality. This article presents 4KODEC, a codec for supporting real-time high-quality videos for cloud-based video streaming. 4KODEC is designed to induce minimal latency when encoding and decoding 4K video frames, by exploiting the fact that many regions (tiles) of a 4K frame can be recovered from their 2K counterparts given the low-complexity embedded in most tiles. Furthermore, 4KODEC is designed to effectively identify complex tiles, so that original 4K representations can be selectively exchanged to maintain high quality. We present evaluations using four different 4K-quality online games to show that 4KODEC reduces the processing latency by nearly half while improving perceptual quality when compared to widely used H.264/265 codec-based 4K video streaming implementations.

Video streaming application with post-decoding concealment technique

WiMAX employes high speed wireless technology and enables high bit rates real time video streaming in wireless environment. Many researchers have proposed various techniques and methods to evaluate and improve video transmission over WiMAX. However, dropped packets seem always exist. This paper implements java based video streaming application over WiMAX network and examines whole frame post-decoding concealment method to reduce dropped packet effect. The result is that packet drop impact in frame can be reduced with PSNR improvement about x dB. However, as concealment performed after decoding, there is additional delay in displaying the image which causes short flicker.

Enhanced QoS Through Optimized Architecture for Video Streaming Applications in Heterogeneous Networks

The exponential growth in the number of internet users for utilizing diversified applications, such as Voice over IP(VoIP), video on demand (VoD), video streaming and e-learning, has resulted in a steady increase in traffic on the transmission channel. Despite video streaming being a large contributor of traffic to both commercial and non-commercial uses, it still lags in the proper delivery of data to the users of cellular devices and another heterogeneous network. As the popularity of video streaming services increases, so does the user’s expectation for quality services. The poor quality of video streaming services has now become absolute in the video stream ecosystem. Therefore, video service providers have taken providing good Quality of Service to their users as their major goal. Provisioning of optimal QoS services in these applications become a challenging task for service providers in the presence of huge traffic and congestion. Since video streaming architecture follows a server-client architecture model, we propose to optimize the functions at the server-side to maximize QoS. The performance of the proposed architecture is compared with the existing architecture in terms of QoS features such as delay, delivery ratio and throughput for various data rates and buffer sizes. The proposed work is simulated using NS2 Simulator. The results show that, end to end delay is reduced by 10%, the delivery ratio is improved by 12% and throughput is improved by 17% when compared to the existing architecture.

Analyzing the effect of adding temporal features to an autoencoder-based video quality model

According to Cisco, most Internet traffic is currently comprised of videos. Therefore, developing a quality assessment method for assuring that those videos are received and displayed with quality at the user side is an important and challenging task. As a consequence, over the last decades, several no-reference video quality metrics have been proposed with the goal of blindly predicting (with no access to the original signal) the quality of videos in streaming applications. One of such metrics is NAVE, whose architecture includes an auto-encoder module that produces a compact set of visual features with a higher descriptive capacity. Nevertheless, the visual features in NAVE do not include descriptive temporal features that are sensitive to temporal degradation. In this work, we analyze the effect on accuracy performance of using a new type of temporal features, based on natural scene statistics. This approach has the goal of making the tested video quality metric more generic, i.e. sensitive to both spatial and temporal distortions and therefore adequate for video streaming applications.

A Survey on Multi-Access Edge Computing Applied to Video Streaming: Some Research Issues and Challenges

Driven by the quality of experience (QoE) requirement of video streaming applications in the smart city, smart education, immersive service, and connected vehicle scenarios, the existing network poses significant challenges, including ultra-high bandwidth, ultra-large storage, and ultra-low latency requirements, etc. Multi-access edge computing (MEC) is a potential technology, which can provide computation-intensive and caching-intensive services for video streaming applications to satisfy the requirement of QoE. Thus, focusing on video streaming schemes, a comprehensive summary of the state of the art applying MEC to video streaming is surveyed. Firstly, the related overview and background knowledge are reviewed. Secondly, resource allocation issues have been discussed. Thirdly, the enabling technologies for video streaming are summarized by taking account of caching, computing, and networking. Then, a taxonomy of MEC enabled video streaming applications is classified. Finally, challenges and future research directions are given.

IEEE Access Special Section Editorial: Lightweight Security and Provenance for Internet of Health Things

As an extension of the Internet of Things (IoT), the Internet of Health Things (IoHT) plays an important role in the remote exchange of data of different physical processes such as patient monitoring, treatment progress, observation, and consultation. In IoHT, connectivity, integration, computation, and interoperability are enabled through various sensors, actuators, and controllers, thereby providing seamless connectivity with efficient utilization of resources. With advances in telemedicine, telesurgery, and other healthcare applications, streaming has become an essential part of IoHT. The data traffic in IoHT applications, such as interactive multimedia streaming, real-time image processing, traffic generated from faulty sensors and vital signs, can tolerate packet loss but has stringent delay requirements. On the other hand, video streaming applications cannot tolerate jitter. Similarly, the low-power devices are sensitive to packet loss and the periodic physiological traffic of medical traffic can tolerate delay or jitter, but not packet loss. Routing data in different IoHT applications has varying quality-of-service (QoS) requirements in terms of delay, packet loss, jitter, and throughput.

An Adaptive Video Transmission Mechanism over MEC‐Based Content‐Centric Networks

The rapid growth of video traffic poses serious challenges to the current Internet. Content‐Centric Networking (CCN) as a promising candidate has been proposed to reengineer the Internet architecture. The in‐network caching and named content communication model of CCN can enhance the video streaming applications and reduce the network workload. Due to the bandwidth‐consuming characteristic of video streaming, the aggressive transmission of video data will cause a reduction of overall network efficiency. In this paper, we present an adaptive video transmission mechanism over Mobile Edge Computing‐ (MEC‐) based CCN. The computation and storage resources of the MEC server are utilized to facilitate the video delivery. Our mechanism adopts a scalable video coding scheme to adaptively control transmission rate to cope with the network condition variation. To analyse the equilibrium property of the proposed mechanism, an analytical model is deduced by using network utility function and convex programming. We also take into account the packet loss in wired and wireless links and present a MEC assistant loss recovery algorithm. The experiment results demonstrate the performance improvement of our proposed mechanism.

EdgeDASH: Exploiting Network-Assisted Adaptive Video Streaming for Edge Caching

With increasing demand for video streaming applications, exploiting cached content at the network edge becomes paramount to prevent congestion in the link between the wireless access network and the content providers. However, it is often challenging to exploit the caches in current client-driven video streaming solutions due to two key reasons. First, even those clients interested in the same content might request different quality levels as a video content is encoded into multiple qualities to match a wide range of network conditions and device capabilities. Second, the clients, who select the quality of the next chunk to request, are unaware of the cached content at the network edge. Hence, it becomes imperative to develop network-side solutions to exploit caching, in particular for the scenarios in which multiple video clients compete for some bottleneck capacity. In this article, we propose EdgeDASH which is a network-side control logic running at a WiFi AP to facilitate the use of cached video content. In particular, an AP can assign a client station to a video quality different than its request, in case the alternative quality provides a better utility. This includes, for example, a function of bits delivered from the cache, video bit rate, and the buffer stalls. We formulate the quality assignment problem as an optimization problem and develop several heuristics with polynomial complexity. Our simulations show that EdgeDASH facilitates significant cache hits and decreases the buffer stalls only by changing the client’s request by one quality level, however with some increase in session instability. From our analysis, we conclude that EdgeDASH benefits are more visible especially when the clients with identical interests compete for a bottleneck link’s capacity, over the baseline where the clients determine the quality adaptation.

QoE-Based Mobility-Aware Collaborative Video Streaming on the Edge of 5G

Today's Internet traffic is dominated by video streaming applications transmitted through wireless/cellular interfaces of mobile devices. Although ultrahigh-definition videos are now easily transmitted through mobile devices, video quality level that users perceive is generally lower than expected due to distance-based high latency between sources and end-users. Mobile edge computing (MEC) paradigm is expected to address this issue and provide users with higher perceived quality of experience (QoE) for latency-critical applications, deploying MEC servers at edges. However, due to capacity concerns on MEC servers, a more comprehensive approach is needed to meet users’ expectations applying all possible operations over the resources such as caching, prefetching, and task offloading policies depending on the data repetition or memory/CPU utilization. To address these issues, this article proposes a novel collaborative QoE-based mobility-aware video streaming scheme deployed at MEC servers. Throughout the article, we demonstrate how the proposed scheme can be implemented so as to preserve the desired QoE level per user during entire video sessions. Performance of the proposed scheme has been investigated by extensive simulations. In comparison to existing schemes, the results illustrate that high efficiency is achieved through collaboration among MEC servers, utilizing explicit window size adaptation, collaborative prefetching, and handover among the edges.

A reliable peer-to-peer streaming protocol in low-capacity networks

The recent global demand for video streaming applications has paved the way for peer-to-peer streaming system (P2PSS). Strategic scheduling scheme and dynamic overlay topology are essential to maintain quality of service (QoS) and quality of experience (QoE) in P2PSS. The concept of P2PSS was tailored towards relying on active peers’ bandwidth to achieve cheap and scalable means of distribution over the Internet, such that peers with highest bandwidth serve as backbones for others. However, selecting backbone peers in low-capacity network environment is challenging due to insufficient bandwidth and poor infrastructure, thereby resulting in poor QoS and unpleasant user’s QoE. In this paper, we conducted a survey on users’ experiences with live video in selected locations in Nigeria. We designed an adaptive P2P streaming protocol and performed a packet-level simulation in Network Simulator 3(NS-3). Diverse simulation scenarios were set up to evaluate the proposed streaming protocol. Trace files data were analysed to measure end-to-end delay, start-up delay, and throughput. Furthermore, the proposed streaming protocol was benchmarked against selected existing schemes. The evaluation results revealed a 7.4% and 28% reduction in start-up and in end-to-end delays and 9% increase in throughput.

Performance Analysis of Video On-demand and Live Video Streaming using Cloud based Services

The advent of Cyber-Physical Systems (CPS) has brought a revolutionary change coined as a mixture of information, communication, computation, and control. With applications in smart grid, health monitoring, automatic avionics, distributed robotics, etc., CPS is currently an area of attention among the academia and industry. The advancement of mobile communications and embedded technology has made it possible to build large scale CPS consisting of the interconnection of mobile phones. These devices collect information about the surrounding environment at any time anywhere basis through real-time video capture. Video streaming has proven to be a massive industry that is growing rapidly playing an important role in everyday life. Customer-driven approach wanting best experience with quality has to be the core offering of contemporary scenario. Video streaming is categorized into Video-On-Demand Streaming (VoDS) and Live Video Streaming (LVS) showing the current state-of-art opportunities. Many diverse applications of video streaming are military video surveillance using drones, live sports match player face recognition, on-demand video characters recognition, movie summarization like identifying parts of the movie which are viewed many times by different users, movie and series recognition, motion detection, gesture recognition, image segmentation, etc. This paper introduces an approach to develop video analysis on VoD and LVS using cloud-based services and analyzes the impact of Quality of Experience (QoE), cost, and bandwidth on the cloud. To achieve the best user experience for video streaming and video analysis, Content Delivery Network (CDN) offers the best QoE at various analyzed locations using various cloud providers like Amazon Web Services (AWS) CloudFront, Google Cloud CDN, Azure CDN, Akamai CDN, etc.

BFM: A Bus-Like Data Feedback Mechanism Between Graphics Processor and Host CPU

Graphics processors have received an increasing attention with the growing demand for gaming, video streaming, and many other applications. During the graphics rendering with OpenGL, host CPU needs the runtime attributes to move on to the next procedure of rendering, which covers almost all the function units of graphics pipeline. Current methods suffer from the memory capacity issues to hold the variables or huge amount of data passing paths which can cause congestion on the interface between graphics processor and host CPU. This paper refers to the operation principle of commuting bus, and proposes a bus-like data feedback mechanism (BFM) to traverse all the pipeline stages and collect the run-time status data or execution error of graphics rendering, then send them back to the host CPU. BFM can work in parallel with the graphics rendering logic. This method can complete the data feedback task easily with only 0.6% increase of resource utilization and has no negative impact on performance, which also obtains 1.3 times speed enhancement compared with a traditional approach.

On accounting for screen resolution in adaptive video streaming: QoE‐driven bandwidth sharing framework

SummaryScreen resolution along with network conditions are main objective factors impacting the user experience, in particular for video streaming applications. User terminals on their side feature more and more advanced characteristics resulting in different network requirements for good visual experience. Previous studies tried to link mean opinion score (MOS) to video bitrate for different screen types (e.g., Common Intermediate Format [CIF], Quarter Common Intermediate Format [QCIF], and High Definition [HD]). We leverage such studies and formulate a Quality of Experience (QoE)‐driven resource allocation problem to pinpoint the optimal bandwidth allocation that maximizes the QoE over all users of a network service provider located behind the same bottleneck link, while accounting for the characteristics of the screens they use for video playout. For our optimization problem, QoE functions are built using curve fitting on datasets capturing the relationship between MOS, screen characteristics, and bandwidth requirements. We propose a simple heuristic based on Lagrangian relaxation and Karush Kuhn Tucker (KKT) conditions to efficiently solve the optimization problem. Our numerical simulations show that the proposed heuristic is able to increase overall QoE up to 20% compared to an allocation with a TCP look‐alike strategy implementing max‐min fairness.

Towards secure and network state aware bitrate adaptation at IoT edge

Video streaming is critical in IoT systems, enabling a variety of applications such as traffic monitoring and health caring. Traditional adaptive bitrate streaming (ABR) algorithms mainly focus on improving Internet video streaming quality where network conditions are relatively stable. These approaches, however, suffer from performance degradation at IoT edge. In IoT systems, the wireless channels are prone to interference and malicious attacks, which significantly impacts Quality-of-Experience (QoE) for video streaming applications. In this paper, we propose a secure and network-state-aware solution, SASA, to address these challenges. We first study the buffer-level constraint when increasing bitrate. We then analyze the impact of throughput overestimation in bitrate decisions. Based on these results, SASA is designed to consist of both an offline and an online phase. In the offline phase, SASA precomputes the best configurations of ABR algorithms under various network conditions. In the online phase, SASA adopts an online Bayesian changepoint detection method to detect network changes and apply precomputed configurations to make bitrate decisions. We implement SASA and evaluate its performance using 429 real network traces. We show that the SASA outperforms state-of-the-art ABR algorithms such as RobustMPC and Oboe in the IoT environment through extensive experiments.