Mobile Streaming Research Articles

What Drives VOD Purchases in Mobile TV Services? Exploring Utilization, Motivations, and Personality Traits

Mobile streaming is increasingly viewed as a major advancement in the wireless industry, as it enables users to consume content without any time or space restrictions. Mobile TV serves as an excellent example of streaming, providing services for watching TV content on mobile devices. While previous studies have explored video-on-demand (VOD) purchasing factors in mobile TV, it is rare to find research examining differences based on users’ mobile TV usage types, such as subscribers and free users. Consequently, we investigated VOD purchase factors for 310 subscribers and 311 free mobile TV users. In other words, using 621 survey responses, we analyze the influence of personality traits, intrinsic and extrinsic motivations, and mobile-related factors on users’ VOD purchase intentions and behavior. Our findings indicate that mobile TV utilization, hedonic needs, and subjective norms are positively related to VOD purchases, and that neuroticism, extraversion, openness, and conscientiousness positively impact mobile TV utilization. We also examine the relationships between the constructs within two sub-groups to highlight the differing perceptions and behavioral patterns of these groups regarding mobile TV utilization and VOD purchases. Theoretical and practical implications are discussed as well.

Toward Low-Latency and High-Quality Adaptive 360$^\circ$ Streaming

Advanced 360-degree video streaming is essential for high-quality communication and industrial video applications, supporting novel and interactive visual experiences that promote consumption. However, due to the inherent fluctuations in communication networks, hardware resources, and power costs, a trade-off needs to be achieved between occupying network bandwidth and guaranteeing 360-degree visual quality. To address this issue, this paper proposes a quality-aware global optimization solution that combines different types of sensory characteristics to further enhance the 360-degree streaming efficiency in cellular networks. First, considering the characteristics of 360-degree video, each frame is divided into three different regions based on a proposed field-of-view (FoV) prediction method. Second, a new region-based rate-distortion model reflecting the bitrate-quality relationship is proposed. The divided regions are represented by different rate-distortion models. In addition, a rate-distortion model parameter update strategy with robustness to region changes is proposed to further guarantee transmission performance. Finally, we propose a globally optimized adaptive bitrate allocation algorithm to optimize 360-degree mobile streaming, which uses both the rate-distortion models and viewpoint prediction results. Evaluation results indicate that the proposed method outperforms state-of-the-art approaches in terms of several quality of experience (QoE) objectives under various network conditions.

The privilege of taken for grantedness: On precarity and mobile media

This essay makes a case for more critical inquiry in mobile media research around the privilege of taken for grantedness. As a critical supplement to Richard Ling’s important work on the taken for granted dispersion and embedment of technologies such as the mobile phone or automobile into everyday life, we examine the precarity that such reliance involves. Taking certain media for granted makes other, more invisible vulnerabilities harder to see and acknowledge. We make this case using the example of TikTok, a short-form mobile streaming app that has rapidly become a go-to social media platform worldwide—as well as a massively “visible” infrastructure due to its associated geopolitical tensions and security concerns. In light of recent conversations about banning the platform, TikTok offers an instructive case study for the privilege of taken for grantedness and the deceptively precarious nature of our mobile media practices.

COUNTERSAVIOR: AIoMT and IIoT-Enabled Adaptive Virus Outbreak Discovery Framework for Healthcare Informatics

In the current pandemic, global issues have caused health issues as well as economic downturns. At the beginning of every novel virus outbreak, lockdown is the best possible weapon to reduce the virus spread and save human life as the medical diagnosis followed by treatment and clinical approval takes significant time. The proposed COUNTERSAVIOR system aims at an Artificial Intelligence of Medical Things (AIoMT), and an edge line computing enabled and Big data analytics supported tracing and tracking approach that consumes global positioning system (GPS) spatiotemporal data. COUNTERSAVIOR will be a better scientific tool to handle any virus outbreak. The proposed research discovers the prospect of applying an individual’s mobility to label mobility streams and forecast a virus such as COVID-19 pandemic transmission. The proposed system is the extension of the previously proposed COUNTERACT system. The proposed system can also identify the alternative saviour path concerning the confirmed subject’s cross-path using GPS data to avoid the possibility of infections. In the undertaken study, dynamic meta direct and indirect transmission, meta behavior, and meta transmission saviour models are presented. In conducted experiments, the machine learning and deep learning methodologies have been used with the recorded historical location data for forecasting the behavior patterns of confirmed and suspected individuals and a robust comparative analysis is also presented. The proposed system produces a report specifying people that have been exposed to the virus and notifying users about available pandemic saviour paths. In the end, we have represented 3-D tracker movements of individuals, 3-D contact analysis of COVID-19 and suspected individuals for 24 h, forecasting and risk classification of COVID-19, suspected and safe individuals.

STRENUOUS: Edge-Line Computing, AI, and IIoT Enabled GPS Spatiotemporal Data-Based Meta-Transmission Healthcare Ecosystem for Virus Outbreaks Discovery

COVID-19 is not the last virus; there would be many others viruses we may face in the future. We already witnessed the loss of economy and daily life through the lockdown. In addition, vaccine, medication, and treatment strategies take clinical trials, so there is a need to tracking and tracing approach. Suitably, exhibiting and computing social evolution is critical for refining the epidemic, but maybe crippled by location data ineptitude of inaccessibility. It is complex and time consuming to identify and detect the chain of virus spread from one person to another through the terabytes of spatiotemporal GPS data. The proposed research aims an HPE edge line computing and big data analytic supported virus outbreak tracing and tracking approach that consumes terabytes of spatiotemporal data. The proposed STRENUOUS system discovers the prospect of applying an individual’s mobility to label mobility streams and forecast a virus-like COVID-19 epidemic transmission. The method and the mechanical assembly further contained an alert component to demonstrate a suspected case if there was a potential exposure with the confirmed subject. The proposed system tracks location data related to a suspected subject in the confirmed subject route, where the location data expresses one or more geographic locations of each user over a period. It recognizes a subcategory of the suspected subject who is expected to transmit a contagion based on the location data. System measure an exposure level of a carrier to the infection based on contaminated location data and a subset of carriers connected with the second location carrier. They investigated whether the people in the confirmed subject’s cross-path can be infected and suggest quarantine followed by testing. The proposed STRENUOUS system produces a report specifying that the people have been exposed to the virus.

YouTube Dataset on Mobile Streaming for Internet Traffic Modeling and Streaming Analysis

Around 4.9 billion Internet users worldwide watch billions of hours of online video every day. As a result, streaming is by far the predominant type of traffic in communication networks. According to Google statistics, three out of five video views come from mobile devices. Thus, in view of the continuous technological advances in end devices and increasing mobile use, datasets for mobile streaming are indispensable in research but only sparsely dealt with in literature so far. With this public dataset, we provide 1,081 hours of time-synchronous video measurements at network, transport, and application layer with the native YouTube streaming client on mobile devices. The dataset includes 80 network scenarios with 171 different individual bandwidth settings measured in 5,181 runs with limited bandwidth, 1,939 runs with emulated 3 G/4 G traces, and 4,022 runs with pre-defined bandwidth changes. This corresponds to 332 GB video payload. We present the most relevant quality indicators for scientific use, i.e., initial playback delay, streaming video quality, adaptive video quality changes, video rebuffering events, and streaming phases.

QoE Models for Adaptive Streaming: A Comprehensive Evaluation

Adaptive streaming has become a key technology for various multimedia services, such as online learning, mobile streaming, Internet TV, etc. However, because of throughput fluctuations, video quality may be dramatically varying during a streaming session. In addition, stalling events may occur when segments do not reach the user device before their playback deadlines. It is well-known that quality variations and stalling events cause negative impacts on Quality of Experience (QoE). Therefore, a main challenge in adaptive streaming is how to evaluate the QoE of streaming sessions taking into account the influences of these factors. Thus far, many models have been proposed to tackle this issue. In addition, a lot of QoE databases have been publicly available. However, there have been no extensive evaluations of existing models using various databases. To fill this gap, in this study, we conduct an extensive evaluation of thirteen models on twelve databases with different characteristics of viewing devices, codecs, and session durations. Through experiment results, important findings are provided with regard to QoE prediction of streaming sessions. In addition, some suggestions on the effective employment of QoE models are presented. The findings and suggestions are expected to be useful for researchers and service providers to make QoE assessments and improvements of streaming solutions in adaptive streaming.

Online Teaching Wireless Video Stream Resource Dynamic Allocation Method considering Node Ability

At present, wireless network technology is advancing rapidly, and intelligent equipment is gradually popularized, which rapidly developed the mobile streaming media business. All kinds of mobile video applications have enriched people’s lives by carrying huge traffic randomly. Wireless networks (WNs) are facing an unprecedented burden, which allocates very important wireless video resources. Similarly, in WNs, the network status is dynamic and the terminal is heterogeneous, which causes the traditional video transmission system to fail to meet the needs of users. Hence, Scalable Video Coding (SVC) has been introduced in the video transmission system to achieve bit rate adaptation. However, in a strictly hierarchical traditional computer network, the wireless resource allocation strategy usually takes throughput as the only way to optimize the target, and it is terrible to make more optimizations for scalable video transmission. This article proposed a cross-layer design to enable information to be transmitted between the wireless base station and the video server to achieve joint optimization. To improve users’ satisfaction with video services, the wireless resource allocation problem and the video stream scheduling problem are jointly considered, which keep the optimization space larger. Based on the proposed architecture, we further study the design of wireless resource allocation algorithms and rate-adaptive algorithms for the scenario of multiuser transmission of scalable video in the Long-Term Evolution (LTE) downlink. Experimental outcomes have shown substantial performance enhancement of the proposed work.

Prediction of Hospital Readmission from Longitudinal Mobile Data Streams.

Hospital readmissions impose an extreme burden on both health systems and patients. Timely management of the postoperative complications that result in readmissions is necessary to mitigate the effects of these events. However, accurately predicting readmissions is very challenging, and current approaches demonstrated a limited ability to forecast which patients are likely to be readmitted. Our research addresses the challenge of daily readmission risk prediction after the hospital discharge via leveraging the abilities of mobile data streams collected from patients devices in a probabilistic deep learning framework. Through extensive experiments on a real-world dataset that includes smartphone and Fitbit device data from 49 patients collected for 60 days after discharge, we demonstrate our framework’s ability to closely simulate the readmission risk trajectories for cancer patients.

Optimization of Embedded Mobile Teaching Model Based on Network Streaming Media Technology

Integrating network technology fully into traditional teaching can realize resource sharing to the greatest extent, so the establishment of distance education and network teaching platform has become the inevitable development of the time. The purpose of this paper is to build an embedded mobile teaching model based on network streaming media technology. The technology application, system composition and structure, realization process, and teaching method of the system are introduced in detail. The system energy consumption, bit rate of video information, and buffer technology were optimized, respectively. In this system, the energy consumption optimization method of mobile streaming media is adopted, and Central Processing Unit (CPU) resources are allocated reasonably according to the principle of maximizing rewards, so as to achieve the purpose of reducing power consumption. The results show that the system can effectively ensure the normal transmission of large multimedia information stream data through the network by using streaming media playback technology, and users can control the teaching process through interactive operation, which makes the network multimedia distance teaching based on streaming media develop in an all-round way and bring advanced teaching mode for education.

Design of a Computer Aided System for Self-Learning Vocal Music Singing with the Help of Mobile Streaming Media Technology

Computer-Aided Design and Applications is an international journal on the applications of CAD and CAM. It publishes papers in the general domain of CAD plus in emerging fields like bio-CAD, nano-CAD, soft-CAD, garment-CAD, PLM, PDM, CAD data mining, CAD and the internet, CAD education, genetic algorithms and CAD engines. The journal is aimed at all developers and users of CAD technology to ptovide CAD solutions for various stages of design and manufacturing. The journal publishes all about Computer-Aided Design and Computer-Aided technologies.

Post-Streaming Wastage Analysis – A Data Wastage Aware Framework in Mobile Video Streaming

Mobile video streaming is now ubiquitous among mobile users. This work investigates a less studied and yet significant problem in mobile video streaming – data wastage, i.e., some downloaded video data may not be played back but discarded by video players due to early departure or video skip, thus the bandwidth consumed in transferring them is wasted. Our measurements show that data wastage is significant in practice, e.g., 25.2 percent∼51.7 percent of video data downloaded are in fact wasted. Moreover, substantial data wastage exists not only in current commercial streaming platforms, but also in state-of-the-art adaptive streaming systems proposed in the literature. This work develops a new post-streaming wastage analysis (PSWA) framework to tackle this problem by converting existing adaptive streaming algorithms into data wastage aware versions. PSWA enables the streaming vendors to explicitly control the tradeoff between data wastage and quality-of-experience (QoE). Extensive evaluations show that PSWA can reduce data wastage significantly (e.g., 80 percent) without any adverse impact on QoE. Moreover, it has strong robustness to perform consistently across a wide range of networks. PSWA can be readily implemented into current streaming platforms, and thus offers a practical solution to data wastage for mobile streaming services.

Study on Dynamic Adaptive Bitrate Selection Algorithm for Mobile Streaming Media

Study on Dynamic Adaptive Bitrate Selection Algorithm for Mobile Streaming Media

A PERFORMANCE IMPROVEMENT SCHEME OF CONTENT CENTRIC NETWORKING OVER MULTI-SOURCE CONTENT DISTRIBUTION

Nowadays, Content Centric Networking (CCN) becomes one of the important technologies for enabling future network. In CCN, the contents are cached along the reverse path from the producer to the consumer, and they may be reused for many times without fetched from the original producer. In such kind of operation, the CCN improves network performance by reducing redundant transmissions of popular contents. However, the volume of the content store (CS) located in the gateway/router is constrained, and much smaller than the size of existing Internet contents which would go through the gateway/router, when rich media applications are considered. In order to solve this issue, this paper proposes a novel Multi-Source Content Centric Networking (MS-CCN) scheme by leveraging the concept of Multi-Source Mobile Streaming (MS2). In the MS-CCN model, the caching of each content is not limited in the single server anymore as in conventional CCN model, instead, each content is fragmented and distributed to multiple servers over a large scale network. After experiencing disjoint multi-paths, various content fragments are concatenated at client side. The OPNET Modeler simulation results show that the MS-CCN scheme significantly outperforms the original CCN scheme in terms of network utilization and user's Quality of Experiment (QoE).

A User Mobility-Based Rate Adaptation Approach for Dynamic HTTP Streaming

It is common for users to use smartphones to watch streaming videos when they are walking, taking a bus, or taking a subway. However, the current streaming rate adaptation algorithms are mostly based on the available network throughput or the player buffer occupancy, and the user mobility state is less considered. In order to improve the user experience quality under the mobile streaming media, we propose a user mobility-based rate adaptation approach, which classifies the user mobility state using the random forests algorithm and adopts different rate decision under different mobility states. The experimental results show that compared with popular approaches, our approach reduces the re-buffer events by 24% on average, and reduces the data traffic consumption by 19% on average.

Flink-ER: An Elastic Resource-Scheduling Strategy for Processing Fluctuating Mobile Stream Data on Flink

As real-time and immediate feedback becomes increasingly important in tasks related to mobile information, big data stream processing systems are increasingly applied to process massive amounts of mobile data. However, when processing a drastically fluctuating mobile data stream, the lack of an elastic resource-scheduling strategy limits the elasticity and scalability of data stream processing systems. To address this problem, this paper builds a flow-network model, a resource allocation model, and a data redistribution model as the foundation for proposing Flink with an elastic resource-scheduling strategy (Flink-ER), which consists of a capacity detection algorithm, an elastic resource reallocation algorithm, and a data redistribution algorithm. The strategy improves the performance of the platform by dynamically rescaling the cluster and increasing the parallelism of operators based on the processing load. The experimental results show that the throughput of a cluster was promoted under the premise of meeting latency constraints, which verifies the efficiency of the strategy.

A Question of Time: HQ Trivia and Mobile Streaming Temporality

A Question of Time: HQ Trivia and Mobile Streaming Temporality

Time

Time

A Study on Materials and Development of Wireless Mobile Communication

In modern society, the need for rapid information exchange is increased continuously. As a result, the telecom industry has grown rapidly to more than double size of the global automotive market and has a major impact on the global economy. With the rapid development of the wireless communication market, a new concept of semiconductor materials having better physical and electrical characteristics. In this paper, we study on relationship between material engineering and wireless communication system. We introduce a greedy heuristic algorithm which optimize the wireless network construction for mobile streaming systems.

Quality Measurement of Images on Mobile Streaming Interfaces Deployed at Scale.

With the growing use of smart cellular devices for entertainment purposes, audio and video streaming services now offer an increasingly wide variety of popular mobile applications that offer portable and accessible ways to consume content. The user interfaces of these applications have become increasingly visual in nature, and are commonly loaded with dense multimedia content such as thumbnail images, animated GIFs, and short videos. To efficiently render these and to aid rapid download to the client display, it is necessary to compress, scale and color subsample them. These operations introduce distortions, reducing the appeal of the application. It is desirable to be able to automatically monitor and govern the visual qualities of these small images, which are usually small images. However, while there exists a variety of high-performing image quality assessment (IQA) algorithms, none have been designed for this particular use case. This kind of content often has unique characteristics, such as overlaid graphics, intentional brightness, gradients, text, and warping. We describe a study we conducted on the subjective and objective quality of images embedded in the displayed user interfaces of mobile streaming applications. We created a database of typical "billboard" and "thumbnail" images viewed on such services. Using the collected data, we studied the effects of compression, scaling and chroma-subsampling on perceived quality by conducting a subjective study. We also evaluated the performance of leading picture quality prediction models on the new database. We report some surprising results regarding algorithm performance, and find that there remains ample scope for future model development.