Factors affecting consumers’ willingness to subscribe to over-the-top (OTT) video streaming services in India

2021 ◽  
Vol 65 ◽  
pp. 101534
Author(s):  
Samala Nagaraj ◽  
Soumya Singh ◽  
Venkat Reddy Yasa
Keyword(s):  
Video Streaming ◽  
Factors Affecting ◽  
Streaming Services ◽  
Over The Top

scholarly journals Personalized Online Live Video Streaming Using Softmax-Based Multinomial Classification

2019 ◽  
Vol 9 (11) ◽  
pp. 2297
Author(s):  
Kyeongseon Kim ◽  
Dohyun Kwon ◽  
Joongheon Kim ◽  
Aziz Mohaisen
Keyword(s):  
Video Streaming ◽  
Waiting Time ◽  
Quality Of Experience ◽  
Video Quality ◽  
Live Video ◽  
Streaming Services ◽  
Online Streaming ◽  
Over The Top ◽  
User Content

As the demand for over-the-top and online streaming services exponentially increases, many techniques for Quality of Experience (QoE) provisioning have been studied. Users can take actions (e.g., skipping) while streaming a video. Therefore, we should consider the viewing pattern of users rather than the network condition or video quality. In this context, we propose a proactive content-loading algorithm for improving per-user personalized preferences using multinomial softmax classification. Based on experimental results, the proposed algorithm has a personalized per-user content waiting time that is significantly lower than that of competing algorithms.

Is Over the Top Video Platform the Game Changer over Traditional TV Channels in India? A Niche Analysis

2021 ◽  
pp. 1326365X2110096
Author(s):  
Hashim Hamza Puthiyakath ◽  
Manash Pratim Goswami
Keyword(s):  
Video Streaming ◽  
Consumer Satisfaction ◽  
Niche Overlap ◽  
Niche Theory ◽  
Substantial Impact ◽  
Degree Of Satisfaction ◽  
Over The Top ◽  
Game Changer

Since the outbreak of COVID-19 and the consequent national lockdown, the usage of over the top (OTT) platforms has significantly increased in India. The growing popularity of video streaming has made a substantial impact on the traditional TV channels during pandemic times. The purpose of this study is to examine the competition, coexistence and competitive superiority of OTT and TV in providing consumer satisfaction. The study adopted the niche theory to empirically measure the degree of gratification fulfilled by OTT and TV, the similarity between OTT and TV and the competitive superiority of OTT and TV across seven micro-dimensions of gratification. The data for the study has been gathered from 223 online users across India. The results of the study reflect that OTT provides a higher degree of satisfaction across all seven dimensions of gratification with the greatest difference manifested in the convenience dimension. The niche overlap measures indicated that the highest level of similarity between TV and OTT is in providing gratification in the relaxation dimension, whereas the least similarity was observed in the convenience dimensions. The competitive superiority of OTT surpassed TV in all dimensions with the greatest difference manifested in relaxation.

scholarly journals Improving Perceived Quality of Live Adaptative Video Streaming

Entropy ◽  
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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