High-quality Streaming Research Articles

Broadcasting Adaptation in the Streaming Era: Industrial Transformation in the Digital Revolution

In an era of ever-evolving streaming revolution, the broadcasting industry is facing major transformation to adapt and survive. Broadcasters need to adopt new strategies in business models and monetization, such as combining subscriptions, individual purchases, data-driven advertising, as well as partnerships with large streaming platforms through a hybrid approach. Investing in producing unique and engaging original content is also critical to differentiate yourself from competitors and cater to diverse audience preferences by exploring new genres and formats. In terms of technological infrastructure, increasing network capacity, servers and hardware are needed to support high-quality streaming, as well as paying attention to security and redundancy to ensure continuity of operations. Moreover, data measurement and analytics are key by developing new, relevant metrics to measure content performance, understand consumer behavior and preferences, and optimize monetization strategies. Leveraging data also allows for better personalization of content according to viewers' individual interests. By adopting a comprehensive approach in all these aspects, broadcasters can adapt effectively and achieve long-term success in an increasingly competitive streaming era.

Intelligent Caching for Seamless High-Quality Streaming in Vehicular Networks: A Multi-Agent Reinforcement Learning Approach

Intelligent Caching for Seamless High-Quality Streaming in Vehicular Networks: A Multi-Agent Reinforcement Learning Approach

Ethical conference economies? Reimagining the costs of convening academic communities when moving online

AbstractOnline conferences are widely thought to reduce many of the costs of convening academic communities. From lower carbon emissions, lower fees, less difficulty in attending (particularly for marginalised researchers), and greater accessibility, virtual events promise to address many of the issues that in‐person events take for granted. In this article, we draw on a community economies framing from geographers J.K. Gibson‐Graham to argue for centring the work of convening within efforts to explore reparative possibilities within the academy. Reflecting on the changing costs arising from moving an originally in‐person conference series online, we argue for embracing the opportunities offered. We explore how organising teams might enact alternative values through allocating the material, financial and labour resources traditionally spent for these events differently. We look particularly at how our carbon and financial costs changed, and how, by retaining a fee, we were able to allocate our budgets in ways which redistributed the surplus to participants in need (rather than bolster conference centre profits). We then explore what these changing costs meant in terms of our attendance levels across career stages and geographical locations. Looking at whether our experiment resulted in increased support for online events, we examine the continued ambivalence felt for the virtual. Finally, while we largely explore the benefits of online options, our last section urges caution over assumptions that this move will result in a more sustainable academia, particularly given the intensifications surrounding high quality streaming video, and suggest that we treat current trends as ongoing experiments, rather than solutions.

User Gaze-Driven Adaptation of Omnidirectional Video Delivery Using Spatial Tiling and Scalable Video Encoding

Omnidirectional video is becoming increasingly popular among viewers, but its delivery requires considerable amount of network bandwidth. Today’s streaming services are transmitting the full spatial angle of omnidirectional videos, although most of the transmitted content is not utilized. Due to both limited bandwidth availability and its dynamic fluctuations, adaptive delivery solutions play a key role in supporting high user quality streaming of omnidirectional videos. This paper describes research which extends the MPEG-DASH Spatial Relationship Description by adding scalable video encoding to spatial tiling. It proposes a novel tile-layering based gaze adaptation algorithm for omnidirectional video delivery and employs it in conjunction with multiple tiling schemes. The benefits of the proposed algorithm with diverse tiling schemes are evaluated objectively in terms of bandwidth savings and adaptation latency. The results show a reduction of network bandwidth requirements to about 30% of the original bandwidth value with a low processing latency of 70.87ms.

An Approximation Algorithm for Joint Caching and Recommendations in Cache Networks

Streaming platforms, like Netflix and YouTube, strive to offer high streaming quality (SQ), in terms of bitrate, delays, etc., to their users. Meanwhile, a significant share of content consumption of these platforms is heavily influenced by recommendations. In this setting, the user's overall experience is a product of both the user's interest in a recommended content, i.e., the recommendation quality (RQ), and the SQ of this content. However, network decisions (like caching) that affect the SQ are usually made without considering the recommender's actions. Likewise, recommendations are chosen independently of the potential delivery quality. In this paper, we define a metric of streaming experience (MoSE) that captures the fundamental tradeoff between the SQ and RQ. We aim to jointly optimize caching and recommendations in a generic network of caches, with the objective of maximizing this metric. This is in line with the recent trend for content providers to simultaneously act as Content Delivery Network owners, implying that the same entity may handle both caching and recommendation decisions. We formulate this joint optimization problem and prove that it can be approximated up to a constant factor. To the best of our knowledge, this is the first polynomial algorithm to achieve a constant approximation ratio for the joint problem. Moreover, our numerical experiments show important performance gains of our algorithm over baseline schemes and existing algorithms in the literature.

Flow Sensing-Based Congestion Detection for D2D Streaming on a 5G gNB.

To provide high-quality streaming services in device-to-device (D2D) communications, performance parameters such as encoding rate, decoding rate, and flow rate should be detected and monitored. The proposed algorithm provides a method to detect time streaming for traffic flows in D2D communications, and a sequence to detect rate imbalance. This paper proposes a new FS-CDA (flow sensing-based congestion detecting algorithm) to prevent high congestion rates and assist an optimized D2D streaming service in 5G-based wireless mobile networks. The proposed algorithm detects and controls flow imbalance for streaming segments during D2D communications, and it includes operations such as transmission rate monitoring, rate adjustment functions, and underflow and overflow sensing for these operations. The paper aims to effectively control traffic flow rates caused by adjacent channel bandwidth, high bit rate error, and heterogeneous radio interference, and to enhance the performance of D2D streaming services by performing such operations. The proposed algorithm for D2D streaming services is measured by deriving the individual weight of certain versions of a streaming flow. Based on the given operations, the simulation results indicated that the proposed algorithm has better performance with respect to average congestion control ratio, PSNR, and average throughput than other methods.

Customer-in-Loop Adaptive Supply Chain Migration Model to Enable IoT

Poised to smart citizenry engagement, an unprecedented deluge of high quality streaming services induce a major data traffic challenge in Fourth Generation (4G) bandwidth and coverage, the upcoming smart city expectations cannot be ignored, eventually. The bottlenecks in ever exploiting benefits such as e-Life, to name a few at Mobile Equipment Providers (MEP) tiers, are complemented at Device Dependent Device Independent (D3I) configurations inherent at various tiers of Mobile Service Providers (MSP). While enabling a Supply Chain Management (SCM) augments a unique system support involving the MSPs and MEPs for desired Customer Relationship Management (CRM), Ad hoc Resource Planning (ARP), which we found prevalent in migration scenario from 4G to 5G technology deployment. Despite its complexity both in term of one-to-many and many-to-one across diverse MSP and MEP options, SCM operational objectives sets forth a unique challenge, hence is the main objective for our work presented here. In this paper, we presented a framework to enhance the 4G legacy in mobile service provider capacity for smart city Machine-to-Machine (M2M) backbone. The migration process is assessed with proposed strategic, technical and operational indicators, which demonstrate its adaptability and flexibility while integrating in conventional 4G deployments, especially taking into account radio devices and applications. Web-enabled Software Define Radio devices and applications are used to index the migration cost and support SCM planning and execution. We identify, the decentralization of mobile service providers infrastructure plays a major role in reducing the embedded complexity which often appears as primary bottleneck. MSP as a key player in the elasticity of migration, we presented a platform to support large as well as low MEP-MSP co-deployments. Pareto multi-criteria optimization is used to find the strategic indicators which are primary Transformation Steering Factors (TSF), valid in both device dependent or device independent M2M migration. We expose our result for achieving TSF, while rolling interoperability and reconfiguration of device deployed in a typical volatile inter-MSP or intra-MSPs tiers. Pareto Migration Indicators (MI) are optimized successively progressing across the transformation schemes; relative to base-line MSP services, hence enabling a lucrative choice while elasticity of provider-centric cost depends adaptively on technology legacy and M2M access of User Equipment (UE).

Convolutional Neural Networks for Continuous QoE Prediction in Video Streaming Services

In video streaming services, predicting the continuous user's quality of experience (QoE) plays a crucial role in delivering high quality streaming contents to the user. However, the complexity caused by the temporal dependencies in QoE data and the non-linear relationships among QoE influence factors has introduced challenges to continuous QoE prediction. To deal with that, existing studies have utilized the Long Short-Term Memory model (LSTM) to effectively capture such complex dependencies, resulting in excellent QoE prediction accuracy. However, the high computational complexity of LSTM, caused by the sequential processing characteristic in its architecture, raises a serious question about its performance on devices with limited computational power. Meanwhile, Temporal Convolutional Network (TCN), a variation of convolutional neural networks, has recently been proposed for sequence modeling tasks (e.g., speech enhancement), providing a superior prediction performance over baseline methods including LSTM in terms of prediction accuracy and computational complexity. Being inspired of that, in this paper, an improved TCN-based model, namely CNN-QoE, is proposed for continuously predicting the QoE, which poses characteristics of sequential data. The proposed model leverages the advantages of TCN to overcome the computational complexity drawbacks of LSTM-based QoE models, while at the same time introducing the improvements to its architecture to improve QoE prediction accuracy. Based on a comprehensive evaluation, we demonstrate that the proposed CNN-QoE model can reach the state-of-the-art performance on both personal computers and mobile devices, outperforming the existing approaches.

Adaptive Customer-In-Loop Supply Chain Management For 5G Migration

Despite the ever exploiting benefits in Fourth Generation (4G) Telecom such as e-Life from Etisalat-UAE, vendors’ economical limitation for providing high quality streaming services in limited bandwidth and coverage expectation cannot be ignored. A unique systems support involving the Customer Relationship, Resource Planning, and Supply Chains exist in both Mobile Service Providers and Mobile Equipment Providers. Despite its complexity, the operational objectives of SCM planning may vary greatly from MSP to MSP and MEP to MEP, hence is the main objective for our work presented here. In this paper, we explore the strategic, technical and operational indicators, while migrating legacy 4G MSP deployments into 5G to conform the e-governess needs.

QoE-Driven Adaptive K-Push for HTTP/2 Live Streaming

Dynamic adaptive streaming (DAS) over HTTP has been widely deployed over the Internet. However, due to the pull-based nature of HTTP/1.1, there exists intolerable streaming latency and high request overhead in the current DAS systems. With dynamic k-push , HTTP/2 live streaming promises to achieve low live latency with less overhead and small segment duration. In this paper, we propose a quality of experience (QoE) driven adaptive k-push mechanism (QK-Push) for HTTP/2 live streaming. The client just sends one request to set push length ( $K$ ) and bitrate ( v ) parameters and the server would push back $K$ segments in a batch. To determine k-push parameters, a probabilistic buffer model is first designed to avoid buffer underflow/overflow. Also, three QoE objective functions are designed to ensure the high streaming quality (bitrate), playback continuity, and smoothness. QK-Push casts this multi-objective optimization problem as a Pareto optimal problem . To solve it, a Nash bargaining solution is designed to balance the needs for video quality, bitrate smoothness, and request overhead. Finally, the segments in each push cycle are selected by solving the Nash problem with a discrete space Lagrangian method. We implement an HTTP/2 live streaming prototype system, with the QK-Push algorithm over modified dash.js and media presentation description. To evaluate the performances, the extensive live streaming experiments are carried out over a controllable network test bed and real Internet trace. The results demonstrate that the proposed QK-Push algorithm is able to improve the average bitrate up to 13%, reduce the bitrate oscillations up to 81%, decrease the startup delay up to 58%, and increase the estimate the mean opinion score up to 12% compared to the current HTTP/1.1 system.

CloudWave: Content gathering network with flying clouds

Abstract The necessity of achieving high streaming quality requires to combine the benefits of cloud computing with the verticals of a content delivery network into a robust, reliable, flexible and fault-tolerant system. This paper presents a solution for data acquisition, processing and Internet-enabled distribution of multimedia content. The objective is to propose an elastic content gathering network which handles media files delivered, for instance, by unmanned aerial vehicles, being further served on-demand to end users scattered over the globe. We have implemented the framework architecture, the system components with their attached responsibilities and capabilities, evaluating the performance based on the extensive simulations. The designed framework has been validated in terms of ensured correctness. Experimental results have proven the proper behavior of the built system by handling both types of requests, as for the storage of massive incoming data sets and for distributing content through multiple employed servers strategically placed in the proximity of the initiated requests’ locations. Furthermore, once an autonomous and scalable network has been successfully designed, the number of required surrogate servers dynamically adjusts to consume the multimedia services in a cost-efficient manner.

A Context-aware adaptive algorithm for ambient intelligence DASH at mobile edge computing

Adaptive streaming has recently emerged as a technology enabling high-quality streaming at various bitrates. One of the video streaming challenges remains in research topic nowadays that is choosing optimal segment base on network characteristics and streaming devices, such as network bandwidth, latency, the computational capacities of devices. Researchers have proposed many algorithms to overcome such issues within their predefined conditions. However, those proposed methods do not perform efficiently in the heterogeneous network today. Consequently, in this article, we present research on a context-aware adaptive algorithm for ambient intelligence dynamic adaptive employing mobile edge computing (MEC). Specifically, we apply deep learning in the adaptive algorithm which is installed at the MEC to assist clients in choosing the optimal streaming segments as well as reduce network latency. Furthermore, we apply the multilayer perceptron classifier with data obtained from various experiments of adaptive streaming algorithms then combine them in a general algorithm. In the analysis, we use network simulator NS3 as a tool to carry out the verification of our proposed method. As a result, the proposed research reduces network latency as well as improve quality streaming compared to existing approaches.

MVP2P: Layer-dependency-aware live MVC video streaming over peer-to-peer networks

Multiview video supports observing a scene from different viewpoints. The Joint Video Team (JVT) developed H.264/MVC to enhance the compression efficiency for multiview video, however, MVC encoded multiview video (MVC video) still requires high bitrates for transmission. This paper investigates live MVC video streaming over Peer-to-Peer (P2P) networks. The goal is to minimize the server bandwidth costs whist ensuring high streaming quality to peers. MVC employs intra-view and inter-view prediction structures, which leads to a complicated layer dependency relationship. As the peers’ outbound bandwidth is shared while supplying all the MVC video layers, the bandwidth allocation to one MVC layer affects the available outbound bandwidth of the other layers. To optimize the utilization of the peers’ outbound bandwidth for providing video layers, a maximum flow based model is proposed which considers the MVC video layer dependency and the layer supplying relationship between peers. Based on the model, a layer dependency aware live MVC video streaming method over a BitTorrent-like P2P network is proposed, named MVP2P. The key components of MVP2P include a chunk scheduling strategy and a peer selection strategy for receiving peers, and a bandwidth scheduling algorithm for supplying peers. To evaluate the efficiency of the proposed solution, MVP2P is compared with existing methods considering the constraints of peer bandwidth, peer numbers, view switching rates, and peer churns. The test results show that MVP2P significantly outperforms the existing methods.

Green two‐tiered wireless multimedia sensor systems: an energy, bandwidth, and quality optimisation framework

In wireless multimedia sensor systems (WMSSs), the devices are equipped with multiple energy-constrained camera sensors (CSs) distributed over bandwidth-constrained and lossy wireless channels, in catastrophe-prone areas. Meanwhile, multimedia applications, e.g. video streaming, require considerable energy and bandwidth resources to gain long lifetime and high streaming quality. This study proposes an energy, bandwidth, and quality (EBQ) optimisation framework for green two-tiered WMSSs. The first tier contains the CSs and the second tier includes cluster heads (CHs) selected from the CSs with higher available energy and processing capacity. In the EBQ optimisation framework, a rate allocation optimisation problem is formulated under given constraints of available backhaul bandwidth of the CHs and quality of received videos at base stations (BSs). This problem is solved for optimal encoding rates to packetise each video captured from different environments into multiple descriptions for transmission. Consequently, the average energy consumption per CS is minimised for long lifetime while conserving the bandwidth of the CHs and guaranteeing high quality of received videos for the purpose of monitoring at the BSs. Simulations demonstrate that the proposed EBQ optimisation framework can efficiently enhance the performance of green two-tiered WMSSs in terms of minimum energy consumption, bandwidth efficiency, and high quality.

Complexity of cloud-based transcoding platform for scalable and effective video streaming services

Nowadays, a fast network improves the quality of our daily life and we can enjoy a variety of services over the Internet. Different types of media streaming services have been proposed and utilized as the network speed is now sufficiently fast to deliver high-quality live streaming. Usually, different media streaming services deliver streaming data by using different protocols such as the real-time message protocol (RTMP), real-time streaming protocol (RTSP), and Windows media HTTP streaming protocol (WMSP). In this paper, we propose and implement a cloud-based scalable and cost-effective video streaming transcoding service platform to provide the service of changing real-time streaming protocols (RTMP/RTSP) and codecs (H.263/H.264). A transcoder dispatching problem (TDP) over the cloud platform is also defined, which attempts to serve all the transcoding requests by minimizing the cost of virtual machines. Further, a transcoder dispatching algorithm and an online transcoder dispatching algorithm are proposed for the TDP. These algorithms are implemented on the Amazon EC2 platform. Experimental results demonstrate that by renting different levels of virtual machines dynamically and intelligently, we can provide a scalable and cost-effective transcoding service for bridging heterogeneous streaming media.

A Joint Online Transcoding and Delivery Approach for Dynamic Adaptive Streaming

Dynamic adaptive streaming has emerged as a popular approach for video services in today’s Internet. To date, the two important components in dynamic adaptive streaming, video transcoding that generates the adaptive bitrates of a video and video delivery that streams the videos to users, have been separately studied, resulting in a huge waste of computation and storage resource due to producing and caching different versions of videos regardless of their demands. We conduct extensive measurement studies of video sharing systems, including an IPTV service which streams regular, professionally made videos and an instant video clip sharing service which provides extremely short user-generated videos, as well as the availability of computation resource in conventional content delivery networks (CDNs). Based on the measurement insights, we propose an online joint transcoding and delivery approach for adaptive video streaming. We formulate optimization problems to enable high streaming quality for the users, and low computation and replication costs for the system. In particular, our strategy connects video transcoding and video delivery based on users’ preferences of CDN regions and regional preferences of video versions. We analyze hardness of these problems and design distributed solutions. Extensive trace-driven experiments further demonstrate the superiority of our design.

Incentive Mechanism Design for Heterogeneous Peer-to-Peer Networks: A Stackelberg Game Approach

With high scalability, high video streaming quality, and low bandwidth requirement, peer-to-peer (P2P) systems have become a popular way to exchange files and deliver multimedia content over the internet. However, current P2P systems are suffering from “free-riding” due to the peers’ selfish nature. In this paper, we propose a credit-based incentive mechanism to encourage peers to cooperate with each other in a heterogeneous network consisting of wired and wireless peers. The proposed mechanism can provide differentiated service to peers with different credits through biased resource allocation. A Stackelberg game is formulated to obtain the optimal pricing and purchasing strategies, which can jointly maximize the revenue of the uploader and the utilities of the downloaders. In particular, peers’ heterogeneity and selfish nature are taken into consideration when designing the utility functions for theStackelberg game. It is shown that the proposed resource allocation scheme is effective in providing service differentiation for peers and stimulating them to make contribution to the P2P streaming system.

A Server-based Flow Allocation Scheme for Streaming Services in Data Center Networks

Abstract Streaming services provided by data centers (DCs) have become increasingly popular. The quality of streaming services greatly depends on network conditions, though, such as available bandwidth and delay time characteristics. To realize high quality stream- ing services, selecting an appropriate access network among available ones is necessary. However, in wireless LANs, a station (STA) simply selects an access point (AP) with the maximum received signal strength to connect to the Internet. Therefore, the service quality will deteriorate when access from STAs is concentrated in a specific AP. In this paper, we propose a server-based flow allocation scheme based on collectable information in DC networks to enable high quality streaming services. This scheme collects information, including the packet loss rate of existing flows as well as the available bandwidth of access networks, and then allocates a streaming flow to an appropriate access network based on the collected information. We show the effectiveness of this approach through simulation evaluations.

A trust-based pollution attack prevention scheme in peer-to-peer streaming networks

Nowadays, peer-to-peer (P2P) streaming systems have become a popular way to deliver multimedia content over the internet due to their low bandwidth requirement, high video streaming quality, and flexibility. However, P2P streaming systems are vulnerable to various attacks, especially pollution attacks, due to their distributed and dynamically changing infrastructure. In this paper, by exploring the features of various pollution attacks, we propose a trust management system tailored for P2P streaming systems. Both direct trust and indirect trust are taken into consideration when designing the trust management system. A new direct trust model is proposed. A dynamic confidence factor that can dynamically adjust the weight of direct and indirect trust in computing the trust is also proposed and studied. A novel double-threshold trust utilization scheme is given. It is shown that the proposed trust management system is effective in identifying polluters and preventing them from further sharing of polluted data chunks.

DASH 기반 멀티미디어 스트리밍 서비스에서 사용자 체감품질 향상을 위한 비트율 적응 기법

Dynamic adaptive streaming over HTTP (DASH)는 최근 주목받고 있는 스트리밍 기술이다. DASH 클라이언트는 연속적으로 가용 네트워크 대역폭을 측정하고, 이를 기반으로 향후 다운받을 비디오 세그먼트의 비트율을 결정한다. 본 고에서는 최소 재생 버퍼 레벨을 보존하여 가변적인 네트워크 환경에서도 사용자 체감 품질 저하를 야기하는 재생 끊김 현상, 재생 중 비디오 품질의 급격한 변화 등을 효율적으로 방지하는 비트율 적응 알고리즘인 QoE-enhanced adaptation algorithm over DASH (QAAD)를 제안한다. 또한 DASH 테스트베드 상에 QAAD와 재생 버퍼를 고려한 기존의 알고리즘 (즉, QDASH[9])을 구현하여 실험적으로 성능을 비교 및 분석하였다. 네트워크 환경이 급변하는 환경에서 실험을 수행한 결과, QDASH는 재생 끊김 현상 및 비디오 화질의 잦은 변화가 발생하는 반면, QAAD는 재생 끊김 없이 안정적인 비디오 화질을 제공하는 것을 확인할 수 있었다. Dynamic adaptive streaming over HTTP (DASH) is the most recent and promising technology to support high quality streaming services. In dynamic adaptive streaming over HTTP (DASH), a client consecutively estimates the available network bandwidth and decides the transmission rate for the forthcoming video chunks to be downloaded. In this paper, we propose a novel rate adaptation algorithm called quality of experience QoE-enhanced adaptation algorithm over DASH (QAAD), which preserves the minimum buffer length to avoid interruption and minimizes the video quality changes during the playback. We implemented a DASH test bed and conducted extensive experiments. Experimental results demonstrate that under fluctuating network conditions, QAAD provides seamless streaming with stabilized video quality while the previous buffer-aware algorithm (i.e., QDASH[9]) frequently changes the video quality and undergoes the interruption.