Video Delivery Research Articles

Resource Allocation for Layered Multicast Video Streaming in NOMA Systems

The application of non-orthogonal multiple access (NOMA) to multi-layer multicast video streaming is envisioned to address the explosively growing demand for capacity which is dominated mostly by multimedia content. In this paper, a joint power allocation and subgrouping scheme is developed to enhance the performance of NOMA-based multi-layer multicast systems. An optimization problem is formulated to maximize the overall sum multicast rate whilst satisfying the maximum transmission power and proportional rate constraints. Due to the complexity of the optimization problem, we first derive two power allocation techniques for the 2-layer case considering arbitrary subgrouping which are based on the iterative implementation and closed-form analysis, respectively. We then generalize the low-complexity closed-form solution for the general multi-layer case. This scheme successively allocates power to each layer stream while assuring that the minimum target rate and proportionality are guaranteed, particularly for the transmission rate of the high-priority base layer stream. A sub-optimal joint power allocation and subgrouping scheme is also designed by incorporating the power allocation scheme into the proposed iterative subgrouping techniques. Simulation results show the effectiveness of the power allocation and subgrouping schemes in enhancing the sum multicast rate performance. In addition, the proposed power allocation scheme ensures substantially higher rates for the base layer stream, which is crucial in robust delivery of standard quality video to all users.

Dynamic MBSFN Beam Area Formation in 6G Multibeam Non-Terrestrial Networks

Providing innovative services and ensuring ubiquitous, ultrahigh-density, extreme-data-rate, and very low latency communications are essential for future sixth-generation systems, where the importance of non-terrestrial networks (NTNs) will incredibly rise. However, the management of both the capacity and the radio spectrum remains one of the main challenges, when a huge number of devices require access to dissimilar broadcast services at the same time. To cope with these issues, traditional single-beam satellite systems can be replaced with very high throughput satellite, where multibeam transmissions increase the capacity and improve the spectrum utilization while limiting interbeam interference. In this article, we propose a dynamic multicast/broadcast single-frequency network (MBSFN) beam area formation (D-MBAF) algorithm that dynamically groups beams into dedicated MBSFN beam areas (MBAs) to increase the aggregate data rate (ADR) of the multibeam NTN system and deliver a given video content to all the interested NTN terminals. D-MBAF leverages multicast subgrouping by clustering the NTN terminals into different MBAs that are simultaneously served with different data rates. Furthermore, the D-MBAF algorithm delivers the video content in different flows: the base layer with lower resolution and a set of enhancement layers with higher resolutions. Radio resources are efficiently allocated to avoid interference among the beams belonging to different MBAs. A simulation campaign is carried out under different scenarios to assess the effectiveness of the proposed D-MBAF algorithm in terms of mean throughput, ADR, resource block utilization, and number of sent layers. Obtained results confirm that the D-MBAF algorithm outperforms the single-frequency multibeam transmission and the multilayer video delivery schemes used as benchmarks.

Optimal QoE-Fair Resource Allocation in Multipath Video Delivery Network

A steadily increasing number of users consume videos over the Internet. In current video platforms, players run a control algorithm that dynamically chooses the video bitrate to match the time-varying network bandwidth. Such an algorithm strives to improve the quality individually perceived by users. Consequently, this control architecture leads, in the optimal case, to maximize the average quality perceived collectively by all users rather than to a quality-fair distribution of resources, possibly leading to user abandonment for those users receiving a lower quality. Therefore, we argue that well-designed video delivery networks should gracefully degrade the perceived quality equally for all users when resources become scarce. In this paper, we propose the Multi-Commodity Flow Problem (MCFP) optimization framework to address the issue of designing a QoE-fair optimal allocation strategy. We show how to make the resulting problem tractable for video platforms serving massive audiences. The performance of the proposed optimal fair resource allocation strategy is tested through realistic simulations involving thousands of concurrent users on two real networks by varying both the total load on the network and the system parameters.

Joint Transcoding- and Recommending-Based Video Caching at Network Edges

Edge caching can significantly improve the quality of wireless video services by deploying cache servers at network edges. Recently, video conversion and recommendation have been introduced to improve the caching performance at the edges. Specifically, they work to produce lower quality versions of videos via video converting (for the former) or provide alternative similar videos when requested videos are not available by using video recommendation (for the latter). However, existing work in this aspect has utilized these two techniques separately, which largely limit their capabilities in providing improved video services. In this article, we study how to jointly utilize these two techniques in edge caching for improved caching performance. The objective is to maximally reduce the video delivery delay while satisfying users’ requirements. We first formulate the optimal video caching problem in this case and derive its NP-hardness. We, then, propose an effective transcoding- and recommending-based caching algorithm (TRBA). The TRBA works in a greedy manner to iteratively buffer the most valuable video versions, one video version each time, until no more video version or cache space is available. We define the value of a video version as the reduced delivery delay if this version were buffered divided by the extra cache space required for its buffering. The computational complexity of TRBA is deduced as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(|V|^3|Q|^3)$</tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$|V|$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$|Q|$</tex-math></inline-formula> represent the total number of videos and the number of versions per video, respectively. Numerical results demonstrate that, compared with existing caching algorithms, TRBA can significantly improve the caching performance.

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.

Cooperative Multi-Agent Deep Reinforcement Learning for Dynamic Virtual Network Allocation With Traffic Fluctuations

Network traffic and computing demand have been changing dramatically due to the growth of various types of network services, e.g., high-quality video delivery and operating system (OS) updates. To maximize the utilization efficiency of limited network resources, network resource control technology is required for smooth and quick operation when network demands change. Therefore, we propose a dynamic virtual network (VN) allocation method based on cooperative multi-agent deep reinforcement learning (Coop-MADRL). This method can quickly optimize network resources even while network demands are drastically changing by learning the relationship between network demand patterns and optimal allocation by using deep reinforcement learning (DRL) in advance. The key idea is to use a multi-agent technique for a reinforcement learning (RL) based dynamic VN allocation method, which can reduce the number of candidate actions per agent and can improve the performance for VN allocation. Moreover, a cooperation technique improves the efficiency of VN allocation. From results of a simulation evaluation, Coop-MADRL can calculate effective allocation within 1 s, which reduces the maximum server and link utilization and drastically reduces the constraint violations compared with that of the static VN allocation method. Furthermore, we revealed that the learning with various mixed traffic models could achieve a high generalization performance for all traffic patterns.

Dynamic Rendering-Aware VR Service Module Placement Strategy in MEC Networks

Combining multiaccess edge computing (MEC) technology and wireless virtual reality (VR) game is a promising computing paradigm. Offloading the rendering tasks to the edge node can make up for the lack of computing resources of mobile devices. However, the current offloading works ignored that when rendering is enabled at the MEC server, the rendering operation depends heavily on the environment deployed on this MEC serve. In this paper, we propose a dynamically rendering-aware service module placement scheme for wireless VR games over the MEC networks. In this scheme, the rendering tasks of VR games are offloaded to the MEC server and closely coupled with service module placement. At the same time, to further optimize the end-to-end latency of VR video delivery, the routing delay of the rendered VR video stream and the costs of the service module migration are jointly considered with the proposed placement scheme. The goal of this scheme is to minimize the sum of the network costs over a long time under satisfying the delay constraint of each player. We model our strategy as a high-order, nonconvex, and time-varying function. To solve this problem, we transform the placement problem into the min-cut problem by constructing a series of auxiliary graphs. Then, we propose a two-stage iterative algorithm based on convex optimization and graphs theory to solve our object function. Finally, extensive simulation results show that our proposed algorithm can ensure low end-to-end latency for players and low network costs over the other baseline algorithms.

QoE-Aware Video Delivery in Multimedia IoT Network with Multiple Eavesdroppers

How to deal with the increasing video traffic and diverse service demands while ensuring the security of transmission is an open issue in the multimedia Internet of Things (IoT). This paper addresses this issue and studies a secure delivery scheme under a multicast scenario in the presence of multiple eavesdroppers where small base stations (SBSs) can send videos to users cooperatively. Aiming at potential eavesdroppers, a channel model including artificial noise is introduced to reduce the harm of illegal data acquisition. A network quality of experience (QoE) optimization problem is first formulated to account for video quality and delivery delay. In order to solve the nonconvex problem, the successive convex approximation (SCA) technique is applied to optimize multicast group beamforming, reduce the possibility of multicast video eavesdropping, and select video quality where a heuristic scheme is proposed to maximize the network QoE. The effectiveness of the proposed scheme is finally validated by extensive simulations in terms of algorithm convergence performance and network QoE-enhanced performance.

Selective Storage: Store and Deliver Only What Matters

Video streaming services must satisfy the growing consumer request for having the best video quality (VQ), anywhere, anytime. As such, and to optimize the end-user VQ for various network and end-device capabilities, over-the-top (OTT) video delivery infrastructure massively relies on adaptive bitrate (ABR) streaming technology. In the common scenario, it consists of processing and storing various compressed representations of the content to stream, that is, different couples of resolution and bitrate, also known as the bitrate ladder. As both processing and storage resources are expensive, each content provider must optimize its operating and infrastructure costs. This article discusses the existing limitations in the ABR streaming landscape and introduces an innovative selective storage algorithm that tackles one of the main challenges in ABR streaming: the storage cost. This algorithm reduces up to 30% of the amount of required storage for the same VQ and remains compliant with existing optimizations in the OTT ecosystem.

Learning Tailored Adaptive Bitrate Algorithms to Heterogeneous Network Conditions: A Domain-Specific Priors and Meta-Reinforcement Learning Approach

Internet adaptive video streaming is a typical form of video delivery that leverages adaptive bitrate (ABR) algorithms to provide video services with high quality of experience (QoE) for various users in diverse and unique network conditions. Such heterogeneous network environments, which can be viewed as exogenous input processes, often lead to the unstable performance of ABR algorithms. Unfortunately, learning-based ABR algorithm which generated by state-of-the-art reinforcement learning (RL) technologies achieves <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">good average performance</i> but fails to perform well in all kinds of network conditions. In this work, considering the video playback process as the Input-driven Markov Decision Process (IMDP), we propose <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{A}^{2}$ </tex-math></inline-formula> BR (Adaptation of ABR), a novel meta-RL ABR approach. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{A}^{2}$ </tex-math></inline-formula> BR is mainly composed of an online stage and an offline stage. It leverages meta-RL to learn an initial meta-policy with various network conditions at the offline stage and makes decisions in personalized network conditions at the online stage. At the same time, we continually optimize the meta-policy to the tailor-made ABR policy for varying the current network environment within few shots. Moreover, in order to improve the learning efficiency, we fully utilize domain knowledge for implementing a virtual player to replay the previously experienced network. Using trace-driven experiments on various scenarios including different vehicles, users, network types, and heterogeneous user-preferences, we show that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{A}^{2}$ </tex-math></inline-formula> BR outperforming recent ABR approaches with rapidly adapting to the personalized QoE metrics and specific network conditions. Testbed experimental results also illustrate the superiority of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{A}^{2}$ </tex-math></inline-formula> BR in adapting to the unseen environments.

Strategies Facilitating Video Visit Implementation by a Medical Group Serving a Diverse Population.

IntroductionVideo visits have created new opportunities to enhance access to care, but limited information exists on strategies medical groups can employ to facilitate video visit use by higher-risk patients. Our objective was to identify generalizable strategies to facilitate successful delivery of video visits by systems serving highly diverse patient populations. MethodsThe authors conducted a qualitative study of physicians and staff members in a large group practice with 4.5 million patients with diverse race and ethnicity and socioeconomic status. Semi-structured interviews were conducted between January 2021 and April 2021, with key informants identified via purposive and snowball sampling. Video-recorded interviews were transcribed and analyzed using thematic analysis to identify major themes and subthemes. ResultsThe 42 key informants included regional and medical center leaders, primary care physicians, service managers, and medical assistants. Participants described clinical leadership in technology and multidisciplinary collaboration as crucial to sustained video care adoption. Strategies to facilitate real-time learning included local innovation, rapid communication channels, and psychological safety. The organization offered broad access to frequently updated data reports to help managers and practitioners understand processes, measure performance, and share best practices. Medical assistants and physicians developed new approaches to empathize, tailor interactions with patients, and overcome psychological and technical barriers to connecting via video. ConclusionsKey strategies for sustained video care adoption included clinical leadership articulating its purpose, multidisciplinary collaboration, local innovation, effective data use, empathy, and personalized care. These findings provide a model for how health care systems can foster robust adoption of technologies to serve diverse populations.

Editorial: Video Content Production and Delivery Over IP Networks and Distributed Computing Facilities

Editorial: Video Content Production and Delivery Over IP Networks and Distributed Computing Facilities

Food neophobia as a mechanism of change in video-delivered cognitive-behavioral therapy for avoidant/restrictive food intake disorder: A case study.

The mechanisms through which cognitive-behavioral therapies (CBTs) for avoidant/restrictive food intake disorder (ARFID) may work have yet to be elucidated. To inform future treatment revisions to increase parsimony and potency of CBT for ARFID (CBT-AR), we evaluated change in food neophobia during CBT-AR treatment of a sensory sensitivity ARFID presentation via a single case study. An adolescent male completed 21, twice-weekly sessions of CBT-AR via live video delivery. From pre- to mid- to post-treatment and at 2-month follow-up, we calculated percent change in food neophobia and ARFID symptom severity measures. Via visual inspection, we explored trajectories of week-by-week food neophobia in relation to clinical improvements (e.g., when the patient incorporated foods into daily life). By post-treatment, the patient achieved reductions across food neophobia (45%), and ARFID severity (53-57%) measures and no longer met criteria for ARFID, with sustained improvement at 2-month follow-up. Via visual inspection of week-by-week food neophobia trajectories, we identified that decreases occurred after mid-treatment and were associated with incorporation of a food directly tied to the patient's main treatment motivation. This study provides hypothesis-generating findings on candidate CBT-AR mechanisms, showing that changes in food neophobia were related to food exposures most connected to the patient's treatment motivations. Cognitive-behavioral therapies (CBTs) can be effective for treating avoidant/restrictive food intake disorder (ARFID). However, we do not yet have evidence to show how they work. This report of a single patient shows that willingness to try new foods (i.e., food neophobia), changed the most when the patient experienced a clinical improvement most relevant to his motivation for seeking treatment.

Strategy for improving the football teaching quality by AI and metaverse-empowered in mobile internet environment

The metaverse has influenced the development of science and technology since it was proposed. While artificial intelligence (AI) is still one of the important technologies to solve problems in metaverse. The success of AI in various fields and the development of information technology make the integration of sports industry and AI an inevitable trend. Currently, the traditional physical education is experiencing qualitative changes, and the demand of integrating AI and metaverse into physical education is becoming more and more obvious. Football teaching is an important part of physical education, and Virtual Reality (VR) has the characteristics of immersion, interaction and imagination, which can build virtual and realistic football teaching process. In this study, through 360-degree panoramic VR football teaching videos empowered by the metaverse and K-means algorithm based on machine learning under AI, we study the strategies for improving the quality of football teaching in the mobile Internet environment. Therefore, we propose K-means based optimized 360-degree panoramic VR football teaching video delivery strategy. In addition, we conduct simulation experiments under content delivery networks simulator, and the simulation results show that the proposed strategy is superior to the baselines in terms of proxy server hit ratio, byte hit ratio, mean response time and students quality of experience. Moreover, through the 360-degree panoramic VR football teaching videos learning, students can intuitively analyze the actions and improve the teaching quality. The reconstruction of football teaching environment is beneficial to promote the combination of football teaching and smart learning.

Spectrum Sharing and Power Allocation Optimised Multihop Multipath D2D Video Delivery in Beyond 5G Networks

In this paper, we propose a joint spectrum and power optimisation (SPO) for multi-hop multi-path (MHMP) device-to-device (D2D) video delivery in beyond 5G (B5G/6G) networks, where the system resources such as spectrum, energy, storage, and content of mobile users (MUs), are taken into account. Particularly, the downlink spectrum resources of the sharing users (SUs) are reused by the transmitters (TXs) of D2D hops and the energy resources of the TXs are utilized for D2D communications. We further exploit the videos stored in the caching users (CUs) located more than one D2D hop far away from the requesting users (RUs) to establish MHMP D2D video delivery sessions from the CUs to the RUs. Then, the SPO problem is formulated for the optimal spectrum sharing pairs of SUs and TXs and the optimal transmission powers allocated to the TXs. Genetic algorithms (GAs) are developed to solve the SPO problem with respect to both binary variable (spectrum sharing) and real variable (power allocation). The SPO solution allows the RUs to alternately request the videos not only from the macro base station over conventional cellular networks but also from the CUs over MHMP D2D communications at the highest quality of service, i.e., maximum video delivery capacity and low power consumption. Simulation results are analysed to demonstrate the feasibility of GAs and the benefits of the proposed SPO solution in comparison with other conventional schemes.

Guest Editorial: Deep Learning Assisted Visual IoT Technologies for Critical Infrastructure Protection

The digitalization of business applications, including the critical infrastructure, promises transformative opportunities to society. IoT devices are one of the greatest technological advancements that have significantly changed the landscape of critical infrastructure applications. However, it presents an array of challenges in defining implementation standards and best practices to secure operational control systems associated with critical infrastructures. Further, critical infrastructures such as electric grids, railways, pipelines, and healthcare are susceptible to numerous threats. Effectively managing these infrastructures with conventional IoT devices is extremely time-consuming and expensive. Here, security becomes the primary concern as IoT devices are resource-constrained in nature, and they need cloud or edge computing infrastructures to process data and respond to infrastructure problems. In many cases, when dealing with long-distance surveillance of critical infrastructures, IoT devices face the problem of low channel capacity, which causes larger resource consumption in the delivery of images and videos. In this time gap, any significant deformation to the data sources may result in disastrous consequences. This creates the need for disruptive technologies to mitigate, adapt, and positively respond to various cyber threats across the critical infrastructure.

On Index Coded Video Delivery at the WiFi Edge: Performance and System Design

On Index Coded Video Delivery at the WiFi Edge: Performance and System Design

A Transcoding-Enabled 360° VR Video Caching and Delivery Framework for Edge-Enhanced Next-Generation Wireless Networks

Virtual reality (VR) content, including 360° panoramic video, provides users with an immersive multimedia experience and therefore attracts increasing research and development attention. However, the requirement of high bandwidth and low latency of virtual reality service demand puts forward greater challenges to the current infrastructure, especially mobile networks. Inspired by the sharable nature of virtual reality content tiles, we further considered the potential opportunities for computing, caching, and multicast to address the challenges of transmission of panoramic content. This paper proposes a novel transcoding-enabled VR video caching and delivery framework for edge-enhanced next-generation wireless networks. Firstly, an edge cooperative caching scheme based on multi-agent reinforcement learning is introduced to improve the utilization efficiency of computing and storage resources, and then reduce service delay. Second, a two-tier NOMA-based base station-multicast group matching mechanism is designed to solve the collaboration challenge during the edge delivery process. A series of experiments have demonstrated the advantages of the proposed scheme in terms of cache hit rate, latency and other aspects in comparison with alternative approaches.

An Evidence-Based Roadmap for the Provision of More Equitable Telemedicine.

During the coronavirus disease 2019 pandemic, as a safety-net organization with a substantial percentage of patients of color and with limited English proficiency (LEP), we were wary of furthering health disparities in our community. We analyzed gaps in telemedicine (telephone and video) delivery in our communities, quantified the effects of our tests of change, and began the process of accumulating evidence to create a road map for other organizations. We leveraged Lean problem-solving strategies to identify modifiable gaps across multiple domains that could inhibit equity in telemedicine. We implemented tests of change across domains of community engagement, technology, education, and access. We observed the proportion of telemedicine encounters across races and languages between April and November, 2020. Regression analyses tested the impact of race and language on telemedicine controlling for age, gender, insurance, and time. Several rounds of changes and enhancements were associated with changes in telemedicine use of +5.5% (p < 0.0001) for Hispanic, +4.0% (p < 0.0001) for Spanish-speaking, -2.1% for Black (p < 0.05), and -4.4% for White patients (p < 0.001). African-American, Hispanic, and non-English-speaking patients had between 2.3 and 4.6 times the odds of preferring telephone to video encounters (p < 0.0001), with increases in preferences for video use over time (p < 0.05). Our roadmap to improve equitable delivery of telemedicine was associated with a significant improvement in telemedicine use among certain minority populations. Most populations of color used telephone more often than video. This preference changed over time and with equity-focused changes in telemedicine delivery.

An Overview of the MPEG Standard for Storage and Transport of Visual Volumetric Video-Based Coding

The increasing popularity of virtual, augmented, and mixed reality (VR/AR/MR) applications is driving the media industry to explore the creation and delivery of new immersive experiences. One of the trends is volumetric video, which allows users to explore content unconstrained by the traditional two-dimensional window of director’s view.The ISO/IEC joint technical committee 1 subcommittee 29, better known as the Moving Pictures Experts Group (MPEG), has recently finalized a group of standards, under the umbrella of Visual Volumetric Video-based Coding (V3C). These standards aim to efficiently code, store, and transport immersive content with 6 degrees of freedom. The V3C family of standards currently consists of three documents: 1) ISO/IEC 23090-5 defines the generic concepts of volumetric video-based coding and its application to dynamic point cloud data; 2) ISO/IEC 23090-12 specifies another application that enables compression of volumetric video content captured by multiple cameras; and 3) ISO/IEC 23090-10 describes how to store and deliver V3C compressed volumetric video content. Each standard leverages the capabilities of traditional 2D video coding and delivery solutions, allowing for re-use of existing infrastructures which facilitates fast deployment of volumetric video.This article provides an overview of the generic concepts of V3C, as defined in ISO/IEC 23090-5. Furthermore, it describes V3C carriage related functionalities specified in ISO/IEC 23090-10 and offers best practices for the community with respect to storage and delivery of volumetric video.