Video Streaming Research Articles

FQ-SAT: A fuzzy Q-learning-based MPQUIC scheduler for data transmission optimization

In the 5G and beyond era, multipath transport protocols, including MPQUIC, are necessary in various use cases. In MPQUIC, one of the most critical issues is efficiently scheduling the upcoming transmission packets on several paths considering path dynamicity. To this end, this paper introduces FQ-SAT - a novel Fuzzy Q-learning-based MPQUIC scheduler for data transmission optimization, including download time, in heterogeneous wireless networks. Different from previous works, FQ-SAT combines Q-learning and Fuzzy logic in an MPQUIC scheduler to determine optimal transmission on heterogeneous paths. FQ-SAT leverages the self-learning ability of reinforcement learning (i.e., in a Q-learning model) to deal with heterogeneity. Moreover, FQ-SAT facilitates Fuzzy logic to dynamically adjust the proposed Q-learning model’s hyper-parameters along with the networks’ rapid changes. We evaluate FQ-SAT extensively in various scenarios in both simulated and actual networks. The results show that FQ-SAT reduces the single-file download time by 3.2%–13.5% in simulation and by 4.1%–13.8% in actual network, reduces the download time of all resources up to 20.4% in web browsing evaluation, and reaches percentage of on-time segments up to 97.5% in video streaming, compared to state-of-the-art MPQUIC schedulers.

Is video streaming hurting box office revenues at U.S. theaters?

ABSTRACT Given the scarcity of data on video streaming consumption, few studies have attempted to measure its effects on other traditional film media. To overcome this literature gap, we evaluate the impact of streaming indirectly by exploring the early introduction of Netflix streaming services in the U.S. as an intervention. We use the Australian market as a comparison group, given that Netflix was not available in that country until 2015. Using a sample of films released in the U.S. and Australia between 2004 and 2014, we document that the launch of Netflix streaming services in 2007 is associated with a reduction of 14 to 17% in box office revenues.

Design of a Leaf-Bottom Pest Control Robot with Adaptive Chassis and Adjustable Selective Nozzle

Pest control is an important guarantee for agricultural production. Pests are mostly light-avoiding and often gather on the bottom of crop leaves. However, spraying agricultural machinery mostly adopts top-down spraying, which suffers from low pesticide utilization and poor insect removal effect. Therefore, the upward spraying mode and intelligent nozzle have gradually become the research hotspot of precision agriculture. This paper designs a leaf-bottom pest control robot with adaptive chassis and adjustable selective nozzle. Firstly, the adaptive chassis is designed based on the MacPherson suspension, which uses shock absorption to drive the track to swing within a 30° angle. Secondly, a new type of cone angle adjustable selective nozzle was developed, which achieves adaptive selective precision spraying under visual guidance. Then, based on a convolutional block attention module (CBAM), the multi-CBAM-YOLOv5s network model was improved to achieve a 70% recognition rate of leaf-bottom spotted bad point in video streams. Finally, functional tests of the adaptive chassis and the adjustable selective spraying system were conducted. The data indicate that the adaptive chassis can adapt to diverse single-ridge requirements of soybeans and corn while protecting the ridge slopes. The selective spraying system achieves 70% precision in pesticide application, greatly reducing the use of pesticides. The scheme explores a ridge-friendly leaf-bottom pest control plan, providing a technical reference for improving spraying effect, reducing pesticide usage, and mitigating environmental pollution.

ESFPNet: Efficient Stage-Wise Feature Pyramid on Mix Transformer for Deep Learning-Based Cancer Analysis in Endoscopic Video.

For patients at risk of developing either lung cancer or colorectal cancer, the identification of suspect lesions in endoscopic video is an important procedure. The physician performs an endoscopic exam by navigating an endoscope through the organ of interest, be it the lungs or intestinal tract, and performs a visual inspection of the endoscopic video stream to identify lesions. Unfortunately, this entails a tedious, error-prone search over a lengthy video sequence. We propose a deep learning architecture that enables the real-time detection and segmentation of lesion regions from endoscopic video, with our experiments focused on autofluorescence bronchoscopy (AFB) for the lungs and colonoscopy for the intestinal tract. Our architecture, dubbed ESFPNet, draws on a pretrained Mix Transformer (MiT) encoder and a decoder structure that incorporates a new Efficient Stage-Wise Feature Pyramid (ESFP) to promote accurate lesion segmentation. In comparison to existing deep learning models, the ESFPNet model gave superior lesion segmentation performance for an AFB dataset. It also produced superior segmentation results for three widely used public colonoscopy databases and nearly the best results for two other public colonoscopy databases. In addition, the lightweight ESFPNet architecture requires fewer model parameters and less computation than other competing models, enabling the real-time analysis of input video frames. Overall, these studies point to the combined superior analysis performance and architectural efficiency of the ESFPNet for endoscopic video analysis. Lastly, additional experiments with the public colonoscopy databases demonstrate the learning ability and generalizability of ESFPNet, implying that the model could be effective for region segmentation in other domains.

Leveraging Edge Computing for Video Data Streaming in UAV-Based Emergency Response Systems.

The rapid advancement of technology has greatly expanded the capabilities of unmanned aerial vehicles (UAVs) in wireless communication and edge computing domains. The primary objective of UAVs is the seamless transfer of video data streams to emergency responders. However, live video data streaming is inherently latency dependent, wherein the value of the video frames diminishes with any delay in the stream. This becomes particularly critical during emergencies, where live video streaming provides vital information about the current conditions. Edge computing seeks to address this latency issue in live video streaming by bringing computing resources closer to users. Nonetheless, the mobile nature of UAVs necessitates additional trajectory supervision alongside the management of computation and networking resources. Consequently, efficient system optimization is required to maximize the overall effectiveness of the collaborative system with limited UAV resources. This study explores a scenario where multiple UAVs collaborate with end users and edge servers to establish an emergency response system. The proposed idea takes a comprehensive approach by considering the entire emergency response system from the incident site to video distribution at the user level. It includes an adaptive resource management strategy, leveraging deep reinforcement learning by simultaneously addressing video streaming latency, UAV and user mobility factors, and varied bandwidth resources.

Operational Tests for Delay-Tolerant Network between the Moon and Earth Using the Korea Pathfinder Lunar Orbiter in Lunar Orbit

The Korea Pathfinder Lunar Orbiter (KPLO) was launched on 5 August 2022, equipped on the SpaceX Falcon 9 launch vehicle. At present, the KPLO is effectively carrying out its scientific mission in lunar orbit. The KPLO serves as a cornerstone for the development and validation of Korean space science and deep space technology. Among its payloads is the DTNPL, enabling the first-ever test of delay-tolerant network (DTN) technology for satellites in lunar orbit. DTN technology represents a significant advancement in space communication, offering stable communication capabilities characterized by high delay tolerance, reliability, and asymmetric communication speeds—a necessity for existing satellite and space communication systems to evolve. In this paper, we briefly give an overview of the Korea Lunar Exploration Program (KLEP) and present scientific data gathered through the KPLO mission. Specifically, we focus on the operational tests for DTN-ION conducted for message and file transfer, as well as real-time video streaming, during the initial operations of the KPLO. Lastly, this study offers insights and lessons learned from KPLO DTNPL operations, with the goal of providing valuable guidance for future advancements in space communication.

Designing transport scheme of 3D naked-eye system

3D naked-eye is constructed from multi-stream as a typical representation of stereoscopic video. Its enormous data volume and stringent low-delay transport requirements pose significant challenges for high-quality real-time transport. Through analysis and experimental verification that current streaming media transport frameworks using the server–client or peer-to-peer scheme face difficulties when transmitting 3D naked-eye in a one-to-one format. Besides, the existing bandwidth estimation algorithms cannot achieve the expected performance when dealing with delay-sensitive traffic. This results in low bandwidth utilization and slow bandwidth estimation, rendering it unfeasible to deliver multi-stream on time. We propose an effective transport framework with different modules for real-time multi-stream and introduce an Agent-to-Agent transport scheme that provides many-to-one connection as the main implementation way of 3D naked-eye transport framework. Additionally, we propose a direct bandwidth estimation algorithm to quickly match network bandwidth for low-delay transport. The Agent terminal centrally processes consolidates transports, and provides macro-level management of multiple video streams. The algorithm directly detects the available bandwidth using packet interval and packet rate models. Finally, using rate decision algorithm arbitrates the results to directly measure the maximum available bandwidth of the link. The Agent-to-Agent achieves 99% bandwidth utilization, addressing the limitations of existing streaming schemes in handling concurrent data streams. Our algorithm provides precise bandwidth estimates with minimal time overhead, meeting the requirements of a delay-sensitive 3D naked-eye system across diverse environments.

Link between Wi-Fi, cordless devices, mobile phone usage patterns, and behavioral problems among Japanese children: A prospective cohort study

BackgroundWith the recent advent of technology, it is important to confirm the health and safety of the youth. This study aimed to prospectively evaluate the relationship between Wi-Fi, cordless phones, and mobile phone usage patterns and behavioral problems. MethodsThis study involved 2465 children aged 8–17 years from the Hokkaido Study on Environment and Children’s Health from October 2020 to January 2021, with a follow-up from September 2021 to March 2022. The mother–child dyad provided information on the presence of residential Wi-Fi and cordless phones, cordless phone call duration, and mobile phone usage pattern (duration of calls using mobile network and internet, online audio streaming, online video streaming, and playing online games) via a baseline questionnaire. Based on the scores on Strength and Difficulties Questionnaire at baseline and follow-up, the children were categorized into four groups: normal, persistent, improved, and concurrent. ResultsNo significant association was found between Wi-Fi, mobile phone calls via mobile networks, and behavioral problems. Cordless phone at home had higher odds for improvement in total difficulty scores, and cordless phone for calling more than 4 min per week had lower odds of persistent problematic prosocial behavior. Longer duration of mobile phone calling via the internet (>40 min/week) had higher odds of concurrent total difficulties. Mobile phone calling via mobile network for <5 min per week had higher odds for improved total difficulty scores. Audio streaming via mobile phones for 60–120 min had lower odds of persistent total difficulties. ConclusionOur results showed sporadic findings between residential RF-EMF indoor sources and mobile phone usage pattern. These observed findings could be affected by residual confounding and chance findings. Ongoing follow-up studies are necessary to further explore this association through detailed exposure assessment and addressing the potential limitations of our study.

Modeling the non-uniform retinal perception for viewport-dependent streaming of immersive video

Modeling the non-uniform retinal perception for viewport-dependent streaming of immersive video

What Brazilianness looks like: SVODs’ impact on cultural representation

This article investigates the complex interplay between subscription video on-demand services (SVODs) and the representation of Brazilianness in global media. Focusing on Brazil’s video streaming landscape, our study delves into the challenges of representing the diverse tapestry of national culture, particularly in content commissioned by foreign platforms. These challenges include technical barriers, such as the concentration of audiovisual production infrastructure in the Rio-São Paulo axis, and textual hurdles, like the perpetuation of stereotypes and exoticization of Brazilian narratives. Our investigation includes a review of historical limited portrayal of Brazilianness on domestic television, an analysis of SVOD content from 2016 to 2023, and a case study of two Brazilian original series: Cangaço Novo and How to be a Carioca. These series illuminate different aspects of Brazilian reality. Cangaço Novo depicts a less fictionalized ‘Brasil’ prevalent in mainstream media, featuring themes of drought, the sertão, and local resistance. In contrast, How to be a Carioca presents a more stereotyped ‘Brazil’ tailored for international audiences, focusing on preconceived notions about Rio de Janeiro. Our findings reveal a paradox in the role of SVODs: they perpetuate national stereotypes even as they provide global access to Brazilian content. These platforms shape and export perceptions of Brazil, influencing how the country is viewed both domestically and globally. We explore the tensions between local content creation and global distribution, emphasizing the complexities of globalization, diversity, and cultural identity within the markets of the Majority World.

Inferring Video Streaming Quality of Real-Time Communication Inside Network

Inferring Video Streaming Quality of Real-Time Communication Inside Network

End-to-End Traffic Flow Rate Estimation From MPEG4 part-2 Compressed Video Streams

End-to-End Traffic Flow Rate Estimation From MPEG4 part-2 Compressed Video Streams

What is ‘children’s television’ in the streaming era?: Assessing content discoverability through Australian children’s streaming platform fluencies

In line with international trends, increasing numbers of children in Australia use streaming video platforms to watch television on-demand from extensive catalogues. Child viewers thus tend to negotiate platform interfaces organised by algorithmic curation to select content, rather than accessing content via scheduled linear TV. The deeper implications of this substantial shift in child audience habits around television have yet to be robustly reckoned with across scholarly and national policy approaches. Indeed, policy settings in Australia have not kept pace with these transformations, one result of which has been that 84% less Australian content was aired on free-to-air commercial broadcasters in 2022 compared to 2019. Key producer bodies fear the sector is in serious peril and may not withstand the current instability. Given that local children’s television meets Australian children’s best interests by situating them within their own socio-cultural context, the issue has become a site of significant policy, industry, and cultural concern. At this precarious time for the Australian children’s television sector, this article outlines key findings of a mixed method study with Australian children aged 7–9 ( n = 37) and their adult guardians to illustrate how children understand, identify, and discover ‘local’ and ‘children’s’ content on streaming platforms. This child audience research contributes to current policy and scholarly debates around the ‘routes to content’ audiences develop in the streaming era. A focus of our analysis is how and if children find Australian content. Our aim is to shed light on how ‘discoverability’ issues compound the current state of turmoil for the sector. We elucidate children’s digital fluencies with platform interfaces but highlight their limited cultural literacies with the content itself, which poses significant implications for industry and policy strategy around local content discoverability for child audiences on streaming platforms.

The investigation of traditional models and machine learning models in dynamic facial expression recognition

In everyday life, dynamic facial expressions are merely continuous human responses to external events. However, in human-computer interaction, rapidly recognizing changes in facial expressions from video streams is a relatively complex process. This complexity renders Dynamic Facial Expression Recognition (DFER) a critical research task in the domains of computer vision and image processing. This paper analyses the correlations and contrasts between static and dynamic facial expression research, highlighting key issues in the study of dynamic facial expressions, such as dynamic feature extraction and frame extraction. After that, it enumerates significant algorithms in both traditional models and deep learning models, providing an analysis of the advantages and disadvantages of these two major approaches. At the same time, it investigates the reasons behind the transition of research models for DFER from traditional methods to deep learning approaches. The paper focuses on two notable models from each approach: Histogram of Oriented Gradient (HOG) for processing raw images, Support Vector Machine (SVM) for data classification in traditional models. Convolutional Neural Network (CNN) for spatial feature extraction and Long Short-Term Memory (LSTM) for temporal feature extraction in deep learning models. These models are discussed in detail concerning their strengths and weaknesses, operational processes, and performance outcomes. In the concluding section, the author summarizes the main factors influencing research in this field and the current challenges encountered. By focusing on future research directions, the paper also presents a review of recent methodologies and offers insightful research directions for further investigation.

AI for quality enhancement of video streaming for ageing adults’ entertainment in rural areas

AI for quality enhancement of video streaming for ageing adults’ entertainment in rural areas

Impact of 5GTechnology on Mobile Internet Usage in Japan

Purpose: To aim of the study was to analyze the impact of 5G technology on mobile internet usage in Japan. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The rollout of 5G technology in Japan has led to a 35% increase in mobile internet usage, with users enjoying faster speeds and improved online experiences. This enhancement supports high-bandwidth applications like video streaming and gaming, and benefits businesses by facilitating remote work and innovative services such as augmented reality (AR) and the Internet of Things (IoT). However, substantial infrastructure investments are needed to ensure widespread coverage and reliability. Unique Contribution to Theory, Practice and Policy: Diffusion of innovations theory, technology acceptance model (TAM) &amp; network externalities theory may be used to anchor future studies on impact of 5G technology on mobile internet usage in Japan. Practically, businesses and mobile service providers in Japan can leverage 5G to develop and deploy innovative services such as augmented reality (AR) applications, real-time gaming experiences, and immersive virtual communication platforms. Policymakers can develop frameworks that facilitate fair competition among telecom operators while promoting investment in 5G infrastructure.

QoS-Aware Power-Optimized Path Selection for Data Center Networks (Q-PoPS)

Data centers consume significant amounts of energy, contributing indirectly to environmental pollution through greenhouse gas emissions during electricity generation. According to the Natural Resources Defense Council, information and communication technologies and networks account for roughly 10% of global energy consumption. Reducing power consumption in Data Center Networks (DCNs) is crucial, especially given that many data center components operate at full capacity even under low traffic conditions, resulting in high costs for both service providers and consumers. Current solutions often prioritize power optimization without considering Quality of Service (QoS). Services such as video streaming and Voice over IP (VoIP) are particularly sensitive to loss or delay and require QoS to be maintained below certain thresholds. This paper introduces a novel framework called QoS-Aware Power-Optimized Path Selection (Q-PoPS) for software-defined DCNs. The objective of Q-PoPS is to minimize DCN power consumption while ensuring that an acceptable QoS is provided, meeting the requirements of DCN services. This paper describes the implementation of a prototype for the Q-PoPS framework that leverages the POX Software-Defined Networking (SDN) controller. The performance of the prototype is evaluated using the Mininet emulator. Our findings demonstrate the performance of the proposed Q-PoPS algorithm in three scenarios. Best-case: Enhancing real-time traffic protocol quality without increasing power consumption. midrange-case: Replacing bottleneck links while preserving real-time traffic quality. Worst-case: Identifying new paths that may increase power consumption but maintain real-time traffic quality. This paper underscores the need for a holistic approach to DCN management, optimizing both power consumption and QoS for critical real-time applications. We present the Q-PoPS framework as evidence that such an approach is achievable.

Synchronising a stereoscopic surgical video stream using specular reflection.

A stereoscopic surgical video stream consists of left-right image pairs provided by a stereo endoscope. While the surgical display shows these image pairs synchronised, most capture cards cause de-synchronisation. This means that the paired left and right images may not correspond once used in downstream tasks such as stereo depth computation. The stereo synchronisation problem is to recover the corresponding left-right images. This is particularly challenging in the surgical setting, owing to the moist tissues, rapid camera motion, quasi-staticity and real-time processing requirement. Existing methods exploit image cues from the diffuse reflection component and are defeated by the above challenges. We propose to exploit the specular reflection. Specifically, we propose a powerful left-right comparison score (LRCS) using the specular highlights commonly occurring on moist tissues. We detect the highlights using a neural network, characterise them with invariant descriptors, match them, and use the number of matches to form the proposed LRCS. We perform evaluation against 147 existing LRCS in 44 challenging robotic partial nephrectomy and robotic-assisted hepatic resection video sequences with simulated and real de-synchronisation. The proposed LRCS outperforms, with an average and maximum offsets of 0.055 and 1 frames and 94.1±3.6% successfully synchronised frames. In contrast, the best existing LRCS achieves an average and maximum offsets of 0.3 and 3 frames and 81.2±6.4% successfully synchronised frames. The use of specular reflection brings a tremendous boost to the real-time surgical stereo synchronisation problem.

Multi‐parameter fusion driver fatigue detection method based on facial fatigue features

AbstractFatigued driving is one of the main causes of traffic accidents. In order to improve the detection speed of fatigue driving recognition, this paper proposes a driver fatigue detection method based on multi‐parameter fusion of facial features. It uses a cascaded Adaboost object classifier to detect faces in video streams. The DliB library is employed for facial key point detection, which locates the driver's eyes and mouth to determine their states. The eye aspect ratio (EAR) is calculated to detect eye closure, and the mouth aspect ratio (MAR) is calculated to detect yawning frequency and count. The detected percentage of eye closure (PERCLOS) value is combined with yawning frequency and count, and a multi‐feature fusion approach is used for fatigue detection. Experimental results show that the accuracy of blink detection is 91% and the accuracy of yawn detection is 96.43%. Furthermore, compared to the models mentioned in the comparative experiments, this model achieves two to four times faster detection times while maintaining accuracy.

Online Scene Semantic Understanding Based on Sparsely Correlated Network for AR.

Real-world understanding serves as a medium that bridges the information world and the physical world, enabling the realization of virtual-real mapping and interaction. However, scene understanding based solely on 2D images faces problems such as a lack of geometric information and limited robustness against occlusion. The depth sensor brings new opportunities, but there are still challenges in fusing depth with geometric and semantic priors. To address these concerns, our method considers the repeatability of video stream data and the sparsity of newly generated data. We introduce a sparsely correlated network architecture (SCN) designed explicitly for online RGBD instance segmentation. Additionally, we leverage the power of object-level RGB-D SLAM systems, thereby transcending the limitations of conventional approaches that solely emphasize geometry or semantics. We establish correlation over time and leverage this correlation to develop rules and generate sparse data. We thoroughly evaluate the system's performance on the NYU Depth V2 and ScanNet V2 datasets, demonstrating that incorporating frame-to-frame correlation leads to significantly improved accuracy and consistency in instance segmentation compared to existing state-of-the-art alternatives. Moreover, using sparse data reduces data complexity while ensuring the real-time requirement of 18 fps. Furthermore, by utilizing prior knowledge of object layout understanding, we showcase a promising application of augmented reality, showcasing its potential and practicality.