Live Video Delivery Research Articles

Super-resolution with perceptual quality for improved live streaming delivery on edge computing

According to Cisco projections, mobile connection throughput is anticipated to more than triple by the year 2023, with the expected typical 5G connection throughput reaching 575 Mbps. Additionally, smartphones and TV sets equipped with computational capabilities for decoding 4K videos have significantly redefined the landscape of Internet video traffic, accounting for 82% of the total Internet traffic. This surge in connection throughput, coupled with the escalating demand for video-centric content, presents an unprecedented challenge in effectively managing traffic across the backhaul infrastructure of 5G networks. In this article, we introduce and evaluate the On-edge enhanced live streaming with super-resolution (ELiveSR) framework for live video delivery, which takes advantage of throughput improvements, in the access networks, and makes use of real-time super-resolution procedure to reduce transmission rate demands in the backhaul segment of the network. This SR procedure is carried by a deep neural network based model and is capable of coping with video content of different subjects. Experimental assessments conducted revealed that the proposed framework can reduce video-related traffic in the backhaul segment by up to 88.37%. Additionally, it simultaneously enhances the quality of experience metrics observed during live video streaming sessions.

Comparison between Live Delivery and Video Delivery of Skincare Products

This article will start with the development trend and current situation of the skincare industry, marketing methods, product styles, online celebrity industry, and marketing methods on skincare products from the perspective of marketing. The study highlights the internal and external differences between marketing methods and skincare products, analyzing the reasons for the above differences. It offers relevant information from the perspective of marketing methods, skincare product industry development and online celebrity marketing suggestions. The need of research on the skincare product relates closely and potentially from microeconomic of skincare products sale to macroeconomic of online platform sales, which stays in line with the trend of both fashion and technology. Research finds out that short video marketing is less influential than live streaming marketing in general. The effect on the skincare products of both expensive luxury and small-time retailer product (relevantly cheap) is of no significant difference. The application of the skincare practice suggests a possible and feasible way in marketing and expanding the skincare products. It also implies a way of upgrading, directing and functioning the purpose and functions of the novel media platform which plays a role in the marketing of products, including but not restricted to skincare ones.

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.

Live Video Mind-Body Program for Patients With Knee Osteoarthritis, Comorbid Depression, and Obesity: Development and Feasibility Pilot Study.

BackgroundKnee osteoarthritis (KOA) is the most common joint disorder in the United States and a leading cause of disability. Depression and obesity are highly comorbid with KOA and accelerate knee degeneration and disability through biopsychosocial mechanisms. Mind-body physical activity programs can engage biological, mechanical, and psychological mechanisms to improve outcomes in KOA, but such programs are not currently available.ObjectiveThis mixed methods study aims to adapt a mind-body activity program for the unique needs of patients with KOA, depression, and obesity (GetActive-OA) delivered via live video.MethodsParticipants were adults (aged ≥45 years) from rural Kentucky with obesity (BMI≥30 kg/m2), idiopathic KOA with mild to moderate radiographic changes, and elevated depressive symptoms (9-item Patient Health Questionnaire ≥10) recruited from 2 orthopedic centers. In phase 1, we developed GetActive-OA and the study protocol using qualitative focus group feedback from the study population (N=9; 2 focus groups, 90 minutes) and multidisciplinary expertise from clinical psychologists and orthopedic researchers. In phase 2, we explored the initial feasibility, credibility, and acceptability of GetActive-OA, live video delivery, and study procedures via an open pilot with exit interviews (N=5; 1 group). This research was guided by National Institutes of Health (NIH) model stage IA.ResultsPhase 1 qualitative analyses revealed nuanced information about challenges with coping and increasing activity, high interest in a mind-body activity program, program participation facilitators (flexibility with technology) and barriers (amotivation and forgetfulness), and perceived challenges with data collection procedures (blood and urine samples and homework). Phase 2 quantitative analyses showed that GetActive-OA met most a priori feasibility markers: acceptability (80%), expectancy (100%), credibility (100%), clinician adherence (90%), homework adherence (80%), questionnaire data collection (100%), program satisfaction (100%), and safety (100%). Adherence to ActiGraph wear (80% baseline, 20% posttest) and collection of blood samples (60%) were low. Participation in GetActive-OA was associated with signals of improvements in general coping (Cohen d=2.41), pain catastrophizing (Cohen d=1.24), depression (Cohen d=0.88), anxiety (Cohen d=0.78), self-efficacy (Cohen d=0.73), pain (Cohen d=0.39), and KOA symptoms (Cohen d=0.36). Qualitative exit interviews confirmed quantitative findings and provided valuable information to optimize the program and protocol.ConclusionsPatients with KOA, depression, and obesity from rural Kentucky were interested in a live video mind-body activity program. GetActive-OA shows promise; however, the program and protocol require further NIH stage I refinement before formal efficacy testing (NIH model stage II).International Registered Report Identifier (IRRID)RR2-10.1016/j.conctc.2021.100720

Topology Optimization in Hybrid Tree/Mesh-based Peer-to-Peer Streaming System

Peer-to-Peer (P2P) video streaming is the fastest growing application of the Internet. One of the main challenge is to provide a high quality of service through the dynamic behavior of the network because a peer may join or leave anytime. Currently, P2P streaming network exist two types of users: streaming users - who use mobile devices with 3G/4G connection expect to watch the live video immediately and storage users - who use PC with wired Internet will download and then watch the video later. We realized that the streaming users may stop watching live video after a while if they find the video is out of their interest. Users leaving causes dynamic and affect the data delivery. On the other hand, the storage users that are downloading the video do not have the concern of interest and playback quality, until they start to watch the video. Hence, the storage users are relatively more stable than streaming users. This paper, we investigate the strategies on the topology construction and maintenance of P2P streaming systems with storage users are closer to the broadcaster than streaming users. And also we apply our idea on hybrid push-pull protocol that combines the benefits of pull and push mechanisms for live video delivery to provide better video streaming quality

PLVER: Joint Stable Allocation and Content Replication for Edge-Assisted Live Video Delivery

Live streaming services have gained extreme popularity in recent years. Due to the spiky traffic patterns of live videos, utilizing distributed edge servers to improve viewers' quality of experience (QoE) has become a common practice nowadays. Nevertheless, the current client-driven content caching mechanism does not support pre-caching from the cloud to the edge, resulting in a considerable amount of cache misses in live video delivery. By jointly considering the features of live videos and edge servers, we propose PLVER, a proactive live video push scheme to address the cache miss problem in live video delivery. Specifically, PLVER first conducts a one-to-multiple stable allocation between edge clusters and user groups to balance the load of live traffic over the edge servers. It then adopts proactive video replication algorithms to speed up video replication among the edge servers. We conduct extensive trace-driven evaluation, covering 0.3 million Twitch viewers and more than 300 Twitch channels. The results demonstrate that with PLVER, edge servers can carry 28 and 82 percent more traffic than the auction-based replication (ABR) method and the caching on requested time (CORT) method, respectively.

Online Service Function Chain Deployment for Live-Streaming in Virtualized Content Delivery Networks: A Deep Reinforcement Learning Approach

Video delivery is exploiting 5G networks to enable higher server consolidation and deployment flexibility. Performance optimization is also a key target in such network systems. We present a multi-objective optimization framework for service function chain deployment in the particular context of Live-Streaming in virtualized content delivery networks using deep reinforcement learning. We use an Enhanced Exploration, Dense-reward mechanism over a Dueling Double Deep Q Network (E2-D4QN). Our model assumes to use network function virtualization at the container level. We carefully model processing times as a function of current resource utilization in data ingestion and streaming processes. We assess the performance of our algorithm under bounded network resource conditions to build a safe exploration strategy that enables the market entry of new bounded-budget vCDN players. Trace-driven simulations with real-world data reveal that our approach is the only one to adapt to the complexity of the particular context of Live-Video delivery concerning the state-of-art algorithms designed for general-case service function chain deployment. In particular, our simulation test revealed a substantial QoS/QoE performance improvement in terms of session acceptance ratio against the compared algorithms while keeping operational costs within proper bounds.

My Healthy Brain: a multimodal lifestyle program to promote brain health

Background Promoting brain health depends on sustaining healthy behaviors across the lifespan. Yet, public adoption of lifestyle behaviors and knowledge of cognitive decline (CD) prevention remains poor. Our multidisciplinary team developed My Healthy Brain (MHB) to promote a healthy lifestyle (e.g. diet, exercise, alcohol, sleep) and build cognitive reserve (e.g. memory compensatory strategies). Our objective was to demonstrate early proof-of-concept for MHB by exploring the feasibility, acceptability, and improvement in primary lifestyle outcomes as well as secondary outcomes of self-determination and subjective wellbeing. Materials and Methods Older adults with subjective (self-report only) or objective (confirmed by cognitive testing) CD, referred by neurologists to modify lifestyle risk factors (e.g. sedentary), participated in a non-randomized open pilot of MHB (N = 24). Participants completed the 8-week MHB group (90 min each) and pre-post outcome measures. Results MHB met all a-priori set benchmarks, including good feasibility of recruitment (71% of patients screened) and enrollment (75% completed baseline), and good acceptability of treatment (75% completed 6 of 8 sessions and post-testing). Program satisfaction was excellent (100% of participants) and no adverse events were reported. We also observed improvements in primary lifestyle outcomes as well as secondary outcomes of self-determination and subjective well-being. Discussion While MHB demonstrated preliminary feasibility and the potential to modify lifestyle risk factors for CD, the program can be improved. Future work will explore the integration of mindfulness skills with behavioral principles to bolster multidomain lifestyle change, and the live video delivery format to bypass barriers to participation.

Multicast at Edge: An Edge Network Architecture for Service-Less Crowdsourced Live Video Multicast

Using smartphones, tablets, and other portable/handheld devices, we have become more reliant on the video streaming services for entertainment and remote work. Mobile data traffic has grown eighteen folds over the past five years accounting for the majority of IP traffic. YouTube Live, Facebook Live, Twitch, DouYu and other streaming as well as video conferencing services have increased in popularity so at any given moment they serve thousands of live video streams to millions of users. The Enhanced Multimedia Broadcast Multimedia Service (eMBMS) is the standard multicast protocol for 5G networks. Cellular multicast has gained considerable attention to efficiently utilize the limited spectrum to transmit multimedia content to cellular sites with co-located viewers, lowering the cost, and maximizing the Quality of Experience (QoE). However, popular live video content providers use unicast mode for live video delivery and have limited support in the eMBMS service-oriented network architecture. In this paper, we propose an overlay network architecture to augment eMBMS to address the limitations of the standard eMBMS architecture and enable service-less multicast for crowdsourced live video providers. We propose a Virtual Network Function (VNF) service that identifies potential multicast scenarios based on user requests for a live video within a confined area. The VNF Application Server collects information, validates a potential multicast scenario, and initiates an ad-hoc multicast service on the fly. We use a real-world dataset of Facebook Live videos to evaluate the proposed architecture. The simulation results depict considerable advantages in terms of cost, efficiency, and Quality of Experience (QoE). Our results show that the proposed architecture provides significant benefits in bandwidth saving at the backhaul, transit, and RAN links.

No‐reference quality assessment for live broadcasting videos in temporal and spatial domains

Nowadays, live broadcasting video has become increasingly popular and high-quality live broadcasting video is highly needed. In practice, live broadcasting videos usually undergo several processing stages, which inevitably introduce multiple distortions, e. g. frame freezing and intensity mutation, causing the degraded quality of experience. However, little work has been done to the quality evaluation of live broadcasting videos, which may hinder the further development of more advanced live broadcasting video delivery systems. Motivated by this, this study presents a no-reference quality evaluation model for live broadcasting videos (LBVQA) in temporal and spatial domains. In the temporal domain, statistic features are extracted to measure the frame freezing and intensity mutation, and the entropy-based feature is extracted to describe the global jitter. In the spatial domain, blurring is measured based on phase coherence, and abnormal exposure ratio is calculated based on an adaptive threshold. Finally, all features are fed into a backpropagation neural network to train the quality prediction model. Experimental results on the Live Broadcasting Video Database demonstrate the advantages of the proposed metric over the state-of-the-art image and video quality metrics.

Results of a feasibility randomized controlled trial (RCT) of the Toolkit for Optimal Recovery (TOR): a live video program to prevent chronic pain in at-risk adults with orthopedic injuries

BackgroundOrthopedic injuries are the leading cause of hospital admissions in the USA, and many of these patients transition into chronic pain. Currently, there are no evidence-based interventions targeting prevention of chronic pain in patients with orthopedic injuries. We iteratively developed a four-session intervention “The Toolkit for Optimal Recovery” (TOR) which we plan to subsequently test for efficacy in a phase III hybrid efficacy-effectiveness multi-site clinical trial. In order to prevent methodological weaknesses in the subsequent trial, we conducted a feasibility pilot to evaluate the TOR delivered via secure live video versus usual care (UC) in patients with orthopedic injuries from an urban, level I trauma clinic, who screen in as at risk for chronic pain and disability. We tested the feasibility of recruitment, acceptability of screening, and randomization methods; acceptability of the intervention, treatment adherence, and treatment fidelity; satisfaction with the intervention; feasibility of the assessment process at all time points; acceptability of outcome measures for the definitive trial; and within-treatment effect sizes.MethodsWe aimed to recruit 50–60 participants, randomize, and retain them for ~ 4 months. Assessments were done electronically via REDCap at baseline, post-intervention (approximately 5 weeks after baseline), and 3 months later. We followed procedures we intend to implement in the full-scale hybrid efficacy-effectiveness trial.ResultsWe recruited 54 participants and found that randomization and data collection procedures were generally acceptable. The majority of participants were white, educated, and employed. Warm hand-off referrals were more effective than research assistants directly approaching patients for participation without their providers’ engagement. Feasibility of recruitment, acceptability of screening, and randomization were good. Satisfaction with the program, adherence to treatment sessions, and treatment fidelity were all high. There were no technical issues associated with the live video delivery of the TOR. There was minimal missing data and outcome measures were deemed appropriate. Effect sizes for improvement after participation in TOR were moderate to large. There were many lessons learned for future trials.ConclusionsThis study provided evidence of the feasibility of the planned hybrid efficacy-effectiveness trial design when implemented at our home institution. Establishing feasibility of the intervention and study procedures at other trauma centers with more diverse patient populations and different clinical practices is required before a multi-site phase III efficacy-effectiveness trial.Trial registrationClinicalTrials.gov ID: NCT03405610. Registered on January 28, 2018—retrospectively registered.

Personal video delivery with rapid Raptor code decoder

Due to the growing popularity of Internet of People (IoP) and its impacts on human activity, it has quickly become an important research field and hot subject. Since one purpose of IoP is to connect people to people, personal live video delivery that has been popular recently can also be regarded as an important application of IoP. For improving the video quality of video communication services, Raptor code has been adopted in various broadband communication systems. In this paper, we propose a systematic Raptor code decoder based on the group parameters for a group of encoding symbols. For a Raptor code application that can frequently use one or several fixed source block lengths (i.e., the number of source symbols in a source block), we could produce the corresponding group parameters in advance and use them to decode the received encoding symbols more efficiently. For personal live video delivery scenario based on the IPTV delivery, the simulation results show that our decoder is faster than the conventional Raptor code decoder which is adopted by the DVB and 3GPP specifications.

QoE Analysis of a Large-Scale Live Video Streaming Event

Streaming video has received a lot of attention from industry and academia. In this work, we study the characteristics and challenges associated with large-scale live video delivery. Using logs from a commercial Content Delivery Network (CDN), we study live video delivery for a major entertainment event that was streamed by hundreds of thousands of viewers in North America. We analyze Quality-of-Experience (QoE) for the event and note that a significant number of users suffer QoE impairments. As a consequence of QoE impairments, these users exhibit lower engagement metrics.

An efficient hybrid push-pull methodology for peer-to-peer video live streaming system on mobile broadcasting social media

With the rapid growth of wireless communication technology, the availability of highly flexible and video-friendly mobile terminal platforms (such as smartphones and tablets), the emergence of major video content providers (like YouTube, Ustream, and PPTV, which provide a large catalog of attractive contents), Peer-to-Peer (P2P) live video streaming over the wireless and Internet is becoming more and more attractive to users. One of the main challenges is to provide a good quality of service though the dynamic behavior of the network. Traditionally, tree-based model uses a push method, that broadcaster transfers data to other users. This model has low start-up delay. However, there are two main problems in this method: if the bandwidth of an internal node is low, children nodes may lose data and when an internal node failure, other nodes can't receive data until completing the recovery of the tree. On the other hand, mesh-based model uses a pull method, has low bandwidth of a neighbor node by pulling necessary data from a number of neighbor nodes. However, mesh-based model requires large buffers to support pull data from neighbor peers and there is an adjustment between minimum delay by sending pull request and overhead of whole system. So, both models have their own strengths and weaknesses. This paper proposes a new hybrid push-pull live P2P video streaming protocol called MobileCast that combines the benefits of pull and push mechanisms for live video delivery. We present new topology for P2P network with more stable and provide better video streaming quality. Our main goal is to minimize the network end-to-end delay, startup time, overhead, packet loss compared to the pure mesh networks, pure tree networks and provide a good quality of service though the dynamic behavior of the network.

Practical, Real-time Centralized Control for CDN-based Live Video Delivery

Live video delivery is expected to reach a peak of 50 Tbps this year. This surging popularity is fundamentally changing the Internet video delivery landscape. CDNs must meet users' demands for fast join times, high bitrates, and low buffering ratios, while minimizing their own cost of delivery and responding to issues in real-time. Wide-area latency, loss, and failures, as well as varied workloads ("mega-events" to long-tail), make meeting these demands challenging. An analysis of video sessions concluded that a centralized controller could improve user experience, but CDN systems have shied away from such designs due to the difficulty of quickly handling failures, a requirement of both operators and users. We introduce VDN, a practical approach to a video delivery network that uses a centralized algorithm for live video optimization. VDN provides CDN operators with real-time, fine-grained control. It does this in spite of challenges resulting from the wide-area (e.g., state inconsistency, partitions, failures) by using a hybrid centralized+distributed control plane, increasing average bitrate by 1.7x and decreasing cost by 2x in different scenarios.

Optimal tree packing for discretized live rate-adaptive streaming in CDN

Rate-adaptive streaming technologies, such as the Dynamic Adaptive Streaming over HTTP (DASH) standard, provides an efficient and easy solution to stream multimedia in a heterogenous context. However, it reinforces the streaming capacity problem in the core Content Delivery Network (CDN) infrastructure since delivering one video means delivering an aggregation of multiple representations. In particular, for live rate-adaptive streaming, a large set of non-divisible data streams need to be either delivered in whole, or not delivered at all. Previous theoretical models that deal with streaming capacity problems are based on elastic bit rates, and do not capture these emerging features faced by today's CDNs. In this paper, we identify a new, discretized streaming model, for live rate-adaptive video delivery in CDNs. For this model we formulate a general optimization problem and show that it is NP-complete. Then we study two fundamental scenarios that occur in real CDNs. For each of these scenarios, we present a fast, easy to implement, and near-optimal algorithm with performance approximation ratios that are negligible for large networks. These are the first sets of results for the discretized streaming model, and have both practical and theoretical importance in a topic that has become critical.

사용자 기반 가변 대역폭 영상 스트리밍 시스템

Adaptive bitrate (ABR) streaming technology has become an important and prevalent feature in many multimedia delivery systems, with content providers such as Netflix and Amazon using ABR streaming to increase bandwidth efficiency and provide the maximum user experience when channel conditions are not ideal. Where such systems could see improvement is in the delivery of live video with a closed loop cognitive control of video encoding. In this paper, we present streaming camera system which provides spatially and temporally adaptive video streams, learning the user’s preferences in order to make intelligent scaling decisions. The system employs a hardware based H.264/AVC encoder for video compression. The encoding parameters can be configured by the user or by the cognitive system on behalf of the user when the bandwidth changes. A cognitive video client developed in this study learns the user’s preferences( i.e. video size over frame rate) over time and intelligently adapts encoding parameters when the channel conditions change. It has been demonstrated that the cognitive decision system developed has the ability to control video bandwidth by altering the spatial and temporal resolution, as well as the ability to make scaling decisions

원격 의료 진단 시스템을 위한 사용자 기반 적응 대역폭 비디오 시스템

Adaptive bitrate (ABR) streaming technology has become an important and prevalent feature in many multimedia delivery systems, with content providers such as Netflix and Amazon using ABR streaming to increase bandwidth efficiency and provide the maximum user experience when channel conditions are not ideal. Where such systems could see improvement is in the delivery of live video with a closed loop cognitive control of video encoding. In this paper, we present streaming camera system which provides spatially and temporally adaptive video streams, learning the user's preferences in order to make intelligent scaling decisions. The system employs a hardware based H.264/AVC encoder for video compression. The encoding parameters can be configured by the user or by the cognitive system on behalf of the user when the bandwidth changes. A cognitive video client developed in this study learns the user's preferences (i.e. video size over frame rate) over time and intelligently adapts encoding parameters when the channel conditions change. It has been demonstrated that the cognitive decision system developed has the ability to control video bandwidth by altering the spatial and temporal resolution, as well as the ability to make scaling decisions

Enabling near real-time central control for live video delivery in CDNs

User-created live video streaming is marking a fundamental shift in the workload of live video delivery. However, live-video-specific challenges and the viral nature of user-created content makes it difficult for current CDNs to deliver 1) high-quality, 2) highly-scalable, and 3) highly-responsive service. We present the design and implementation of VDN, a new control plane for CDNs designed to optimize the delivery of live streams within the CDN. VDN satisfies these requirements by using two approaches: 1) optimizing directly for video quality (not just throughput) and 2) combining centralized control with local control, allowing VDN to adapt to traffic dynamics and network failures at fine timescales.

A review of multiple description coding techniques for error-resilient video delivery

Multiple description coding (MDC) is one of the promising solutions for live video delivery over lossy networks. In this paper, we present a review of MDC techniques based on their application doma...