Live Video Streaming Services Research Articles

CA-Live360: Crowd-assisted transcoding and delivery for live 360-degree video streaming

In the burgeoning field of digital media, the popularity of live 360-degree video has witnessed an upward trajectory, underpinned by its immersive experience. In live 360-degree video streaming services, transcoding enormous video tiles and providing low-latency services for global viewers require significantly more computing and communication resources than traditional live services. Inspired by the huge crowdsourcing computational resources available at crowd, this paper designs CA-Live360, a Crowd-Assisted Live 360-degree video streaming solution, in which transcoding and delivery are supported by crowdsourcing devices. Due to the dynamic availability of resources, it is challenging to achieve low-latency live services, while guaranteeing fair transcoding. Therefore, firstly an innovative fairness-guaranteed transcoding task assignment scheme is proposed. This scheme employs a novel Fair Bandit algorithm based on the combinatorial multi-armed bandit approach to achieve low-latency transcoding while considering time-varying available resources and fairness constraints. Secondly, to reduce delivery latency, we design a transcoding-aware delivery scheme, in which a provider–requester matching algorithm is introduced to realize effective requester scheduling. Real-world trace-driven experiments demonstrate the improved performance of the proposed CA-Live360 solution in terms of system delay and fairness.

Simulation of a Tele-Surgery process through a Live Video Streaming service, using Simu5G and Wowza

Telematic services that require low latency for real-time applications and that make use of wireless mobile networks are increasingly popular. In the case of Tele-Surgery services that employ Live Video Streaming (LVS), latency times of the order of 1ms are needed. Given the difficulty of implementing real 5G test scenarios that enable this type of service to be characterized, this paper presents an emulation scenario that uses Simu5G to simulate the network, Wowza as a real video server, OBS Studio for transmission and VLC media player for content playback. This emulation scenario makes it possible to modify such parameters as bitrate, bandwidth, frequency and numerology index in order to evaluate different network configurations. By varying these parameters in a controlled way, packet losses are obtained for different bitrate values. The best quality video was obtained with a bitrate of 3000 Kbps.

Cost-effective live video streaming for internet of connected vehicles using heterogeneous networks

Smart vehicles of Internet of Connected Vehicles (IoCV) create Ad-Hoc networks and are persistently connected to the Internet through different network access technologies like Wifi, cellular, and dedicated short range communication (DSRC). A vehicle may prefer one network access technology over another based on availability, coverage area, and network access cost. The choice also depends on application requirements, like delay-tolerant or delay-sensitive, and application objectives, like maximizing data rate, maximizing cellular traffic offloading, etc. Cellular traffic offloading using Wifi access points and vehicle-to-vehicle (V2V) collaborations are very challenging due to the intermittent short coverage areas of Wifi access points and highly dynamic connectivity of V2V Ad-Hoc network. Most existing works on traffic offloading have considered delay-tolerant or on-demand services, whereas delay-constrained live video streaming services are mostly ignored. This work is focused on live video streaming services over IoCVs, where the non-triviality of the network selection task is demonstrated by formulating the task as an integer linear programming problem (ILPP). Accordingly, a distributed algorithm is proposed, which greedily maximizes cellular traffic offloading while significantly reducing the control packet overheads. Compared to existing works, the proposed work provides the best service effectiveness with a balanced performance on traffic offloading, service efficiency, and user satisfaction.

A Hybrid NDN-IP Architecture for Live Video Streaming: From Host-Based to Content-Based Delivery to Improve QoE

With live video streaming becoming accessible in various applications on all client platforms, it is imperative to create a seamless and efficient distribution system that is flexible enough to choose from multiple Internet architectures best suited for video streaming (live, on-demand, AR). In this paper, we highlight the benefits of such a hybrid system for live video streaming as well as present a detailed analysis with the goal to provide a high quality of experience (QoE) for the viewer. For our hybrid architecture, video streaming is supported simultaneously over TCP/IP and Named Data Networking (NDN)-based architecture via operating system and networking virtualization techniques to design a flexible system that utilizes the benefits of these varying Internet architectures. Also, to relieve users from the burden of installing a new protocol stack (in the case of NDN) on their devices, we developed a lightweight solution in the form of a container that includes the network stack as well as the streaming application. At the client, the required Internet architecture (TCP/IP versus NDN) can be selected in a transparent and adaptive manner. Based on a prototype, we have designed and implemented maintaining efficient use of network resources, we demonstrate that in the case of live streaming, NDN achieves better QoE per client than IP and can also utilize higher than allocated bandwidth through in-network caching. Even without caching, as opposed to IP-only, our hybrid setup achieves better average bitrate and better perceived visual quality (computed via VMAF metric) over live video streaming services. Furthermore, we present detailed analysis on ways adaptive video streaming with NDN can be further improved with respect to QoE.

An Intelligent Resource Reservation for Crowdsourced Live Video Streaming Applications in Geo-Distributed Cloud Environment

Crowdsourced live video streaming (livecast) services such as Facebook Live, YouNow, Douyu, and Twitch are gaining more momentum recently. Allocating the limited resources in a cost-effective manner while maximizing the quality of service (QoS) through real-time delivery and the provision of the appropriate representations for all viewers is a challenging problem. In our article, we introduce a machine-learning based predictive resource allocation framework for geo-distributed cloud sites, considering the delay and quality constraints to guarantee the maximum QoS for viewers and the minimum cost for content providers. First, we present an offline optimization that decides the required transcoding resources in distributed regions near the viewers with a tradeoff between the QoS and the overall cost. Second, we use machine learning to build forecasting models that proactively predict the approximate transcoding resources to be reserved at each cloud site ahead of time. Finally, we develop a greedy nearest and cheapest algorithm (GNCA) to perform the resource allocation of real-time broadcasted videos on the rented resources. Extensive simulations have shown that GNCA outperforms the state-of-the-art resource allocation approaches for crowdsourced live streaming by achieving more than 20% gain in terms of system cost while serving the viewers with relatively lower latency.

A Contemporary Survey on Live Video Streaming from a Computation-Driven Perspective

Live video streaming services have experienced significant growth since the emergence of social networking paradigms in recent years. In this scenario, adaptive bitrate streaming communications transmitted on web protocols provide a convenient and cost-efficient facility to serve various multimedia platforms over the Internet. In these communication models, video content is delivered optimally, possibly transcoded, edited automatically, and cached temporarily by network elements along the path. To this end, the computational capabilities of various network elements are considered as major resources to be optimized for service quality improvements. This article provides a contemporary survey of cutting-edge live video streaming studies from a computation-driven perspective. First, an overview of the global standards, system architectures, and streaming protocols is presented. Next, hierarchical computation-driven models of live video streaming are anatomized, including cloud-, edge-, and peer-to-peer-based solutions. Cutting-edge studies are then reviewed to discover the advances they have made in improving system performance in multiple areas. Finally, open challenges are presented to direct future research in this field.

Vertical Handover Decision Making Algorithms for 5G Heterogeneous Networks: A Review

The wireless networks have been rapidly used in our daily lives, especially mobile wireless networks. An environment consisting of several networks such as WLAN, 4G-LTE, and 5G, is known as a heterogeneous network. Wireless networks are widely used to support various data services such as live video streaming, upload data, download data and cloud service depending upon their capacity and nature. When the mobile terminal is moving, the network availability will be changed. Therefore, the mobile terminal must perform handover from one network to another network in heterogeneous networks to maintain the call and quality of service (QoS). This process is known as Vertical Handover (VHO). A seamless handover is important to provide uninterrupted high quality service while performing the handover. To achieve this, various vertical handover decision making algorithms have been proposed and studied using advanced tools and proven concepts. This paper gives an overview of current research on handover decision making in fifth generation (5G) heterogeneous wireless networks. Keywords—Vertical Handover Decision Making, Heterogeneous Networks, Quality of Service, 5G, Seamless Handover

The Upstream Matters: Impact of Uplink Performance on YouTube 360° Live Video Streaming in LTE

Live video streaming services are gaining momentum as network and terminal capabilities improve. However, 360° live video streaming services pose new challenges due to its high bandwidth and computational requirements both on the user and service provider. In this paper, a study of the impact of the uplink of a cellular network on the performance of 360° live video streaming in YouTube is presented. Unlike previous works, the analysis focuses on the upstream between the video source and the server, not on the downstream between the server and viewers. To this end, a measurement campaign is conducted where a live video feed is transmitted and received through YouTube 360° platform in a pilot Long Term Evolution (LTE) system. During the tests, a large dataset of real traces is collected at different protocol layers, both in upstream and downstream, to check the correlation between TCP/IP metrics and key service performance indicators (e.g., video segment quality and end-to-end latency). Results show that uplink performance has a strong impact on the latency perceived by the user, which is critical for the considered live services.

Entrepreneurial Efforts and Opportunity Costs: Evidence from Twitch Streamers

In the last decade, there has been substantial growth in the number of individuals deriving income through digital platforms (e.g., Uber, Airbnb, Instagram), including from live streaming platforms. In this context, we examine whether reduced opportunity costs, such as having more free time and being unemployed, affect people’s entrepreneurial efforts, particularly regarding professionalized streaming activity. As such, we use an extensive longitudinal dataset gathered from the live-video streaming service Twitch.tv, and we exploit the changes and restrictions brought about by the COVID-19 pandemic to measure individuals’ responses to having to spend more time at home, also given that some streamers were more affected than others. By comparing newcomers to established streamers, we find that all streamers intensified their efforts during this period and that this effect was particularly strong for streamers who were somewhat but not fully professionalized prior to the pandemic. This is consistent with the view that low opportunity costs are advantageous for starting and professionalizing activities in the platform labor market, that the work is taken up because of a lack of alternatives and, thus, can provide a form of insurance. This especially holds for streamers who had reached a certain threshold pre COVID-19, as performing at this level enabled them to consider further professionalizing their streaming activities. The ex-post analysis of newcomers’ success shows that even when mobility increased beyond pre-lockdown levels and unemployment rates had again fallen strongly, the most successful “newcomer” streamers were able to transform their initial reaction of potentially only income smoothing to a sustainable long-term solution with the potential for further entrepreneurial effort.

Optimizing Social Welfare of Live Video Streaming Services in Mobile Edge Computing

The live video streaming services have been suffered from the limited backhaul capacity of the cellular core network and occasional congestions due to the cloud-based architecture. Mobile Edge Computing (MEC) brings the services from the centralized cloud to nearby network edge to improve the Quality of Experience (QoE) of cloud services, such as live video streaming services. Nevertheless, the resource at edge devices is still limited and should be allocated economically efficiently. In this paper, we propose Edge Combinatorial Clock Auction (ECCA) and Combinatorial Clock Auction in Stream (CCAS), two auction frameworks to improve the QoE of live video streaming services in the Edge-enabled cellular system. The edge system is the auctioneer who decides the backhaul capacity and caching space allocation and streamers are the bidders who request for the backhaul capacity and caching space to improve the video quality their audiences can watch. There are two key subproblems: the caching space value evaluations and allocations. We show that both problems can be solved by the proposed dynamic programming algorithms. The truth-telling property is guaranteed in both ECCA and CCAS. The simulation results show that the overall system utility can be significantly improved through the proposed system.

Addressing Application Latency Requirements through Edge Scheduling

Latency-sensitive and data-intensive applications, such as IoT or mobile services, are leveraged by Edge computing, which extends the cloud ecosystem with distributed computational resources in proximity to data providers and consumers. This brings significant benefits in terms of lower latency and higher bandwidth. However, by definition, edge computing has limited resources with respect to cloud counterparts; thus, there exists a trade-off between proximity to users and resource utilization. Moreover, service availability is a significant concern at the edge of the network, where extensive support systems as in cloud data centers are not usually present. To overcome these limitations, we propose a score-based edge service scheduling algorithm that evaluates network, compute, and reliability capabilities of edge nodes. The algorithm outputs the maximum scoring mapping between resources and services with regard to four critical aspects of service quality. Our simulation-based experiments on live video streaming services demonstrate significant improvements in both network delay and service time. Moreover, we compare edge computing with cloud computing and content delivery networks within the context of latency-sensitive and data-intensive applications. The results suggest that our edge-based scheduling algorithm is a viable solution for high service quality and responsiveness in deploying such applications.

Client-Side QoE Management for SVC Video Streaming: An FSM Supported Design Approach

HTTP adaptive streaming (HAS) which provides the flexibility of video bit-rate adjustments, is becoming the de facto framework for live and on-demand video streaming services. The flexibility of HAS is further empowered by the scalable video coding (SVC) technique which allows low overhead quality upgradations of transmitted video segments. In this paper, we present a client-side finite-state machine (FSM) supported quality of experience (QoE) centric video bit-rate adaptation and management mechanism. The proposed strategy simultaneously manages three important QoE verticals: 1) providing stutter-free video viewing experience; 2) minimizing flickers in video outputs by controlling and smoothing the rates of encoding quality switches over time; and 3) maximizing aggregate video quality over a video playout session. Based on careful consideration of these QoE verticals, the management policy dynamically decides whether to download a new segment or upgrade the quality of an already downloaded segment in the playout buffer. We have implemented and evaluated the performance of our proposed framework in a multi-client SVC video steaming test-bed. Conducted experiments using real-world network traces reveal that the proposed strategy is able to outperform other state-of-the-art adaptive streaming techniques and deliver satisfactory QoE even in the face of highly varying channel conditions.

An overlay management strategy to improve QoS in CDN-P2P live streaming systems

With the tremendous growth in demand of live video streaming services over Internet, content delivery networks (CDNs) get overloaded. The CDN-P2P live streaming systems are being deployed to reduce the stream delivery load of CDNs. The inherent peer churn and heterogeneity in upload contribution of peers make it challenging to maintain the quality of service (QoS) in these systems. To deal with this challenge, we propose an overlay management strategy that organizes peers in the overlay based on their serviceability, which we define based on stability, stream chunk availability, upload and download capacities of peers. Peers are arranged in a hybrid tree-mesh overlay at the edges of CDN. The peers with higher upload capacity are part of an extended CDN tree to facilitate stable seeders. A part of the upload capacity of the existing peers is reserved to form virtual sources that provide startup chunks for quick playback. The peers joining the mesh overlay select partners with highest serviceability to ensure better streaming quality. They also adapt the topology by replacing partners based on serviceability and upload capacity utilization to maintain QoS during churn. Overall, the proposed serviceability-aware overlay management strategy (SOMS) enhances the QoS and upload capacity utilization of peers while dealing with heterogeneity in upload contribution and peer churn. Comparison with the existing CDN-P2P systems shows that the upload capacity utilization of peers is improved by 30%, while the startup delay and streaming quality are improved by 20% and 25%, respectively.

Virtual network embedding with optimization problems in live video streaming services for regional activation

Virtual network embedding with optimization problems in live video streaming services for regional activation

Statistical validation of an LTE emulation tool using live video streaming over reliable transport protocols

Multimedia services such as Live Video-Streaming (LVS) or Video on Demand are the main drivers behind the notable traffic growth in cellular networks. This fact has attracted the interest of both academia and telcos in improving the end user’s quality of experience. For this reason, the development of test benches for conducting traffic studies in this type of telecommunication system is necessary and emulators are a good option to validate possible solutions without affecting the real systems. However, most works addressing the emulation of Long-Term Evolution (LTE) systems do not validate their outcomes with real environments and hence ignore critical parameters such as the impact of wireless propagation under different scenarios. Therefore, the goal of this paper is to validate an outdoor propagation model for LVS so that accurate studies of traffic characterization in LTE networks can be carried out using an emulation tool. The validation is done through a statistical analysis of variance (ANOVA) of Quality of Service metrics, comparing the results obtained in an LTE-emulated scenario with those obtained in a real LTE test bench. For this study, the LVS service runs on top of a reliable transport protocol. Specifically, we test the Real-Time Message Protocol and the MPEG Dynamic Adaptive Streaming over HTTP (DASH) protocol, both using TCP at the transport layer. According to the results, the propagation model that better matches the real environment under the considered conditions is the Nakagami-m propagation model with m = 5.

Do Real-time Interactions Increase Audience Satisfaction for Online Live Video Streaming Service? - The Case of AfreecaTV in Korea

People’s behavior of multimedia consumption is now changing from passive watching to active or interactive watching. After opening of AfreecaTV, a leading Korean online personal broadcasting service, broadcasting jockeys broadcast their multimedia content, such as videos, shows, sports games and online games, with their own comments. The key feature distinguishing Afreeca TV from traditional broadcasting services is the interactivity between broadcasting jockeys and the audience. This paper tries to examine whether this interactivity could be interpreted as a new powerful viewing motive inducing the audience to choose a certain AfreecaTV channel by applying the uses and gratifications theory. Broadcasting jockey interaction was identified as an important factor that affects audience activity and satisfaction. Real-time interactions with both other audience members of the same interest was also found to be a critical factor in inducing audience to watch online live video streaming services. Along with the self-motivation to interact with other members of the audience, the effort of broadcasting jockeys to interact with audiences could play a critical role in increasing the audience’s motivation to viewing AfreecaTV.

Impact of viewer engagement on gift-giving in live video streaming

This study investigates the effect of viewer engagement on gifting items to a streamer in a live video streaming. Data were collected from AfreecaTV, a leading live video streaming platform in South Korea, to examine viewer engagement and gift-giving behavior. After analyzing 2,294,837 viewers over a three-month period, the empirical results provide evidence that viewer engagement is positively associated with gift-giving decisions. However, the impact of viewer engagement on the amount of gifts purchased is different depending on how the engagement is measured (i.e., by stream or by channel). This study empirically proves that the motive for socialization has a high correlation with gift-giving behavior, which is considered as commoditization of a viewer’s social interaction while consuming media. The study concludes with a discussion on practical implications for live video streaming services and suggestions for future research.

A Lightweight Service Placement Approach for Community Network Micro-Clouds

Community networks (CNs) have gained momentum in the last few years with the increasing number of spontaneously deployed WiFi hotspots and home networks. These networks, owned and managed by volunteers, offer various services to their members and to the public. While Internet access is the most popular service, the provision of services of local interest within the network is enabled by the emerging technology of CN micro-clouds. By putting services closer to users, micro-clouds pursue not only a better service performance, but also a low entry barrier for the deployment of mainstream Internet services within the CN. Unfortunately, the provisioning of these services is not so simple. Due to the large and irregular topology, high software and hardware diversity of CNs, a “careful” placement of micro-clouds services over the network is required to optimize service performance. This paper proposes to leverage state information about the network to inform service placement decisions, and to do so through a fast heuristic algorithm, which is critical to quickly react to changing conditions. To evaluate its performance, we compare our heuristic with one based on random placement in Guifi.net, the biggest CN worldwide. Our experimental results show that our heuristic consistently outperforms random placement by 2x in bandwidth gain. We quantify the benefits of our heuristic on a real live video-streaming service, and demonstrate that video chunk losses decrease significantly, attaining a 37% decrease in the packet loss rate. Further, using a popular Web 2.0 service, we demonstrate that the client response times decrease up to an order of magnitude when using our heuristic. Since these improvements translate in the QoE (Quality of Experience) perceived by the user, our results are relevant for contributing to higher QoE, a crucial parameter for using services from volunteer-based systems and adapting CN micro-clouds as an eco-system for service deployment.

A lightweight service placement approach for community network micro-clouds

Community networks (CNs) have gained momentum in the last few years with the increasing number of spontaneously deployed WiFi hotspots and home networks. These networks, owned and managed by volunteers, offer various services to their members and to the public. While Internet access is the most popular service, the provision of services of local interest within the network is enabled by the emerging technology of CN micro-clouds. By putting services closer to users, micro-clouds pursue not only a better service performance, but also a low entry barrier for the deployment of mainstream Internet services within the CN. Unfortunately, the provisioning of these services is not so simple. Due to the large and irregular topology, high software and hardware diversity of CNs, a “careful” placement of micro-clouds services over the network is required to optimize service performance. This paper proposes to leverage state information about the network to inform service placement decisions, and to do so through a fast heuristic algorithm, which is critical to quickly react to changing conditions. To evaluate its performance, we compare our heuristic with one based on random placement in Guifi.net, the biggest CN worldwide. Our experimental results show that our heuristic consistently outperforms random placement by 2x in bandwidth gain. We quantify the benefits of our heuristic on a real live video-streaming service, and demonstrate that video chunk losses decrease significantly, attaining a 37% decrease in the packet loss rate. Further, using a popular Web 2.0 service, we demonstrate that the client response times decrease up to an order of magnitude when using our heuristic. Since these improvements translate in the QoE (Quality of Experience) perceived by the user, our results are relevant for contributing to higher QoE, a crucial parameter for using services from volunteer-based systems and adapting CN micro-clouds as an eco-system for service deployment.

Towards Agile and Smooth Video Adaptation in HTTP Adaptive Streaming

HTTP Adaptive Streaming (HAS) is widely deployed on the Internet for live and on-demand video streaming services. Video adaptation algorithms in the existing HAS systems are either too sluggish to respond to congestion level shifts or too sensitive to short-term network bandwidth variations. Both degrade user video experience. In this paper, we formally study the tradeoff between responsiveness and smoothness in HAS through analysis and experiments. We show that client-side buffered video time is a good feedback signal to guide video adaptation. We then propose novel video rate control algorithms that balance the needs for video rate smoothness and high bandwidth utilization. We show that a small video rate margin can lead to much improved smoothness in video rate and buffer size. We also propose HAS designs that can work with multiple servers and wireless connections. We develop a fully functional HAS system and evaluate its performance through extensive experiments on a network testbed and the Internet. We demonstrate that our HAS designs are highly efficient and robust in realistic network environment.