Improving Quality of Live Streaming Service over P2P Networks with User Behavior Model

As a popular form of virtual reality (VR) media, omnidirectional video (OV) has been continuously developed in recent years. OV contains the view of the scene in every direction, which will ask for around 120 Mbps with 8k resolution and 25 fps (frames per second). Although there has been a lot of work to optimize the transmission for on-demand of OV, the research on the live streaming of OV is still very lacking. Another big challenge for the OV live streaming system is the huge demand for computing resources. The existing terminal devices are difficult to completely carry tasks such as stitching, encoding, and rendering. This paper proposes a mobile edge assisted live streaming system for omnidirectional video (MELiveOV); the MELiveOV can intelligently offload the processing tasks to the edge computing enabled 5G base stations. The MELiveOV consists of an omnidirectional video generation module, a streaming module, and a viewpoint prediction module. A prototype system of MELiveOV is implemented to prove its complete end-to-end OV live streaming service. Evaluation result demonstrates that compared with the traditional solution, MELiveOV can reduce the network bandwidth requirement by about 50% and the transmission delay of more than 70% while ensuring the quality of the user’s experience.

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A Highly Effective Route for Real-Time Traffic Using an IoT Smart Algorithm for Tele-Surgery Using 5G Networks

Journal of Sensor and Actuator Networks ◽

10.3390/jsan10020030 ◽

2021 ◽

Vol 10 (2) ◽

pp. 30

Author(s):

Radwan S. Abujassar ◽

Husam Yaseen ◽

Ahmad Samed Al-Adwan

Keyword(s):

Live Streaming ◽

Data Traffic ◽

Data Packets ◽

Real Time Traffic ◽

Medical Quality ◽

Optimal Smoothing ◽

Patient Will ◽

Three Stages ◽

Exchange Data

Nowadays, networks use many different paths to exchange data. However, our research will construct a reliable path in the networks among a huge number of nodes for use in tele-surgery using medical applications such as healthcare tracking applications, including tele-surgery which lead to optimizing medical quality of service (m-QoS) during the COVID-19 situation. Many people could not travel due to the current issues, for fear of spreading the covid-19 virus. Therefore, our paper will provide a very trusted and reliable method of communication between a doctor and his patient so that the latter can do his operation even from a far distance. The communication between the doctor and his/her patient will be monitored by our proposed algorithm to make sure that the data will be received without delay. We test how we can invest buffer space that can be used efficiently to reduce delays between source and destination, avoiding loss of high-priority data packets. The results are presented in three stages. First, we show how to obtain the greatest possible reduction in rate variability when the surgeon begins an operation using live streaming. Second, the proposed algorithm reduces congestion on the determined path used for the online surgery. Third, we have evaluated the affection of optimal smoothing algorithm on the network parameters such as peak-to-mean ratio and delay to optimize m-QoS. We propose a new Smart-Rout Control algorithm (s-RCA) for creating a virtual smart path between source and destination to transfer the required data traffic between them, considering the number of hops and link delay. This provides a reliable connection that can be used in healthcare surgery to guarantee that all instructions are received without any delay, to be executed instantly. This idea can improve m-QoS in distance surgery, with trusted paths. The new s-RCA can be adapted with an existing routing protocol to track the primary path and monitor emergency packets received in node buffers, for direct forwarding via the demand path, with extended features.

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User Identification in the Process of Web Usage Data Preprocessing

International Journal of Emerging Technologies in Learning (iJET) ◽

10.3991/ijet.v14i09.9854 ◽

2019 ◽

Vol 14 (09) ◽

pp. 21 ◽

Cited By ~ 1

Author(s):

Jozef Kapusta ◽

Michal Munk ◽

Dominik Halvoník ◽

Martin Drlík

Keyword(s):

User Behavior ◽

Web Server ◽

Web Browser ◽

Ip Address ◽

User Session ◽

Log Files ◽

The Us ◽

Log File ◽

Default Form

If we are talking about user behavior analytics, we have to understand what the main source of valuable information is. One of these sources is definitely a web server. There are multiple places where we can extract the necessary data. The most common ways are to search for these data in access log, error log, custom log files of web server, proxy server log file, web browser log, browser cookies etc. A web server log is in its default form known as a Common Log File (W3C, 1995) and keeps information about IP address; date and time of visit; ac-cessed and referenced resource. There are standardized methodologies which contain several steps leading to extract new knowledge from provided data. Usu-ally, the first step is in each one of them to identify users, users’ sessions, page views, and clickstreams. This process is called pre-processing. Main goal of this stage is to receive unprocessed web server log file as input and after processing outputs meaningful representations which can be used in next phase. In this pa-per, we describe in detail user session identification which can be considered as most important part of data pre-processing. Our paper aims to compare the us-er/session identification using the STT with the identification of user/session us-ing cookies. This comparison was performed concerning the quality of the se-quential rules generated, i.e., a comparison was made regarding generation useful, trivial and inexplicable rules.

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Factors Affecting the Popularity of Video Content on Live-Streaming Services: Focusing on V Live, the South Korean Live-Streaming Service

Sustainability ◽

10.3390/su12051784 ◽

2020 ◽

Vol 12 (5) ◽

pp. 1784 ◽

Cited By ~ 2

Author(s):

Minjeong Ham ◽

Sang Woo Lee

Keyword(s):

Live Streaming ◽

Cumulative Number ◽

Video Content ◽

Factors Affecting ◽

South Korean ◽

Key Characteristics ◽

Quality Video ◽

Streaming Service ◽

High Quality Video ◽

Production Company

Naver V Live, a South Korean live-streaming service, showcases video contents specific to the entertainment industry, such as K-pop and music. On V Live, K-pop stars and their fans can interact directly in a natural way, and V Live provides high-quality video content with novel topics. This study has identified key characteristics of video content that affect its popularity. A total of 620 video contents of five leading Star channels were classified on the basis of production company, type of video content, and whether it was live-streamed or not. The popularity of video content was measured by the number of comments, hearts, and views. To control potential bias, additional variables were set as control variables—such as the number of channel subscribers, mini-album sales, if the video content was previewed, and cumulative number of days since the video content was uploaded. For analysis, a hierarchical linear regression was conducted. The findings suggest future directions in video content planning.

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Improving Perceived Quality of Live Adaptative Video Streaming

Entropy ◽

10.3390/e23080948 ◽

2021 ◽

Vol 23 (8) ◽

pp. 948

Author(s):

Carlos Eduardo Maffini Santos ◽

Carlos Alexandre Gouvea da Silva ◽

Carlos Marcelo Pedroso

Keyword(s):

Video Streaming ◽

Short Term Memory ◽

Transmission Rate ◽

Video Quality ◽

Video On Demand ◽

Live Streaming ◽

Network Congestion ◽

Congested Networks ◽

Over The Top

Quality of service (QoS) requirements for live streaming are most required for video-on-demand (VoD), where they are more sensitive to variations in delay, jitter, and packet loss. Dynamic Adaptive Streaming over HTTP (DASH) is the most popular technology for live streaming and VoD, where it has been massively deployed on the Internet. DASH is an over-the-top application using unmanaged networks to distribute content with the best possible quality. Widely, it uses large reception buffers in order to keep a seamless playback for VoD applications. However, the use of large buffers in live streaming services is not allowed because of the induced delay. Hence, network congestion caused by insufficient queues could decrease the user-perceived video quality. Active Queue Management (AQM) arises as an alternative to control the congestion in a router’s queue, pressing the TCP traffic sources to reduce their transmission rate when it detects incipient congestion. As a consequence, the DASH client tends to decrease the quality of the streamed video. In this article, we evaluate the performance of recent AQM strategies for real-time adaptive video streaming and propose a new AQM algorithm using Long Short-Term Memory (LSTM) neural networks to improve the user-perceived video quality. The LSTM forecast the trend of queue delay to allow earlier packet discard in order to avoid the network congestion. The results show that the proposed method outperforms the competing AQM algorithms, mainly in scenarios where there are congested networks.

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