Significance of Live Streaming in Shaping Business: A Critical Review and Analytical Study

With the rise of live streaming on social media, platforms like Facebook, Instagram, and YouTube have become powerful business tools. They enable users to share live videos, fostering direct connections between businesses and their customers. This critical literature review paper explores the impact of live streaming on businesses, focusing on its role in attracting and satisfying consumers by promoting products tailored to their needs and wants. It emphasizes live streaming’s crucial role in engaging customers, a key to business growth. The study also provides viable strategies for businesses to leverage live streaming for growth and customer engagement, underscoring its importance in the business landscape.

Research on Current Situation and Legal Regulation of Cosmetics Live Streaming E-commerce in China

Analyze the compatibility between cosmetics and live streaming e-commerce from its own nature,marketing means and supply chain characteristics.According to the prominent problems,sort out the relationship between all parties in the cosmetics live e-commerce industry chain.Combined with the latest regulatory policies of live streaming e-commerce and cosmetics,the responsibilities of different subjects in cosmetics live streaming e-commerce are summarized,and relevant suggestions and countermeasures are put forward for the standardization and development of live streaming e-commerce.Cosmetics brand owners are the first responsible persons for product quality.Anchors,as a mixed identity between intermediary,advertising spokesperson and operator,should bear stricter joint and several liability when recommending products related to consumers’health.If anchors fail to clearly identify themselves in the recommendation process,thus causing consumers to mistake them for the operator of the cosmetics,they should assume the obligations of the operator.

SDHT： Efficient One-Hop DHT Lookup Framework for P2P Live Streaming

Video data location plays a key role for Peer-to-Peer （P2P） live streaming applications. In this paper, we propose a new one-hop Distributed Hash Table （DHT） lookup frarrework called Strearre ing-DHT （SDHT） to provide efficient video data location service. By adopting an enhanced events dissemination mechanism-EDRA＋, the accuracy of routing table at peers can be guaranteed. More importantly, in order to enhance the perforlmnce of video data lookup operation without incurring extra overhead, we design a so-called Distributed Index Mapping and Management Mechanism （DIMM） for SDHT. Both analytical modeling and intensive simulation experiments are conducted to demonstrate the effectiveness of SDHT framework. Numerical results show that almost 90% requested video data can be retrieved within one second in SDHT based systems, and SDHT needs only 26% average bandwidth consumption when compared with similar one-hop DHT solutions such as D1HT. This indicates that SDHT framework is an appropriate data lookup solution for time-sensitive network applications such as P2P live streaming.

GPSA:A Greedy Pull-based Scheduling Approach for P2P Live Streaming under Heterogeneous Environments

Pull-based P2P live streaming is a promising solution for the large scale streaming systems, like PPStream, PPlive, due to its high scalability, low cost and high resilience. However, they usually suffer from bad delay performance. In this paper, we seek to improve the delay performance under ensuring video display quality stemming from chunk scheduling. And so we model Pull-based chunk scheduling problem as a multi-objective optimization problem to minimize the video delay and maximize video display quality in the environment of heterogeneous upload bandwidths, heterogeneous and dynamic propagation delays. Finally we put up with a greedy Pull-based scheduling approach(GPSA) to solve the optimization problem. The evaluation shows GPSA can outperform two classical chunk scheduling approaches and adapt to dynamic variance of propagation delays.

A study on TCP performance of crowdsourced live streaming

The prevalence of smart phone and improvement of wireless net promote the usage of crowdsourced live streaming,where individual users act as live streaming sources to broadcast themselves online. Characterizing the performance and identifying its bottleneck in such systems can shed light on the system design and performance optimization. TCP performance of a commercial crowdsourced live streaming system is examined by analyzing packet-level traces collected at streaming servers. TCP stalls that heavily hurt the Qo E of user have been identified. In particular,the TCP stalls account for as much as 31. 6% of the flow completion time for upload flows and result in abandonment of upload on the corresponding channels. Stalls caused by timeout retransmissions are further dissected and timeout retransmission characteristics are revealed to be dependent on the video encoding methods. These findings provide new insights in crowdsourced live streaming systems and can guide designers to improve the TCP efficiency.

Improving Playback Quality of Peer-to-Peer Live Streaming Systems by Joint Scheduling and Distributed Hash Table Based Compensation

In Peer-to-Peer(P2P) streaming systems,video data may be lost since peers can join and leave the overlay network randomly,thereby deteriorating the video playback quality.In this paper we propose a new hybrid mesh and Distributed Hash Table(DHT) based P2P streaming system,called HQMedia,to provide high playback quality to users by maintaining high data dissemination resilience with a low overhead.In HQMedia,peers are classified into Super Peers(SP) and Common Peers(CP) according to their online time.SPs and CPs form a mesh structure,while SPs alone form a new Streaming DHT(SDHT) structure.In this hybrid architecture,we propose a joint scheduling and compensation mechanism.If any frames cannot be obtained during the scheduling phase,an SDHT-based compensation mechanism is invoked for retrieving the missing frames near the playback point.We evaluate the performance of HQMedia by both theoretical analysis and intensive simulation experiments on large-scale networks to demonstrate the effectiveness and scalability of the proposed system.Numerical results show that HQMedia significantly outperforms existing mesh-based and treebased P2P live streaming systems by improving playback quality with only less than 1% extra maintenance overhead.

4D-MAP:Multipath Adaptive Packet Scheduling for Live Streaming over QUIC

In recent years,live streaming has become a popular application,which uses TCP as its primary transport protocol.Quick UDP Internet Connections(QUIC)protocol opens up new opportunities for live streaming.However,how to leverage QUIC to transmit live videos has not been studied yet.This paper first investigates the achievable quality of experience(QoE)of streaming live videos over TCP,QUIC,and their multipath extensions Multipath TCP(MPTCP)and Multipath QUIC(MPQUIC).We observe that MPQUIC achieves the best performance with bandwidth aggregation and transmission reliability.However,network fluctuations may cause heterogeneous paths,high path loss,and band-width degradation,resulting in significant QoE deterioration.Motivated by the above observations,we investigate the multipath packet scheduling problem in live streaming and design 4D-MAP,a multipath adaptive packet scheduling scheme over QUIC.Specifically,a linear upper confidence bound(LinUCB)-based online learning algorithm,along with four novel scheduling mechanisms,i.e.,Dispatch,Duplicate,Discard,and Decompensate,is proposed to conquer the above problems.4D-MAP has been evaluated in both controlled emulation and real-world networks to make comparison with the state-of-the-art multipath transmission schemes.Experimental results reveal that 4D-MAP outperforms others in terms of improving the QoE of live streaming.

Discourse analysis of knowledge-based live streaming:a case study of East Buy streamer Dong Yuhui

Live streaming e-commerce is not only an important part of China’s digital economy but also the preferred way of purchase for many Chinese,especially the young.Since 2022,a new model initiated by East Buy has emerged,quickly becoming known as knowledge-based live streaming.The most famous streamer from this novel type of live streaming is Dong Yuhui,a former English teacher of the New Oriental School.As a top Internet influencer with millions of followers,Dong Yuhui has become a conspicuous social sign of the rising China that is undergoing fast technological,economic,and cultural changes.This study thus takes Dong Yuhui as an example to examine the typical discourses of the knowledge-based live streaming.Through a triangulation of case study,discourse analysis,and sentiment analysis,it finds that Dong Yuhui mainly employs the strategies of disseminating encyclopedic knowledge,telling intriguing stories,and using poetic language in his live streaming discourse.This unique discourse has the main function of fulfilling customers’intellectual,emotional,and psychological needs.As such,it is warmly received by customers,whose comments are centered on the talents of the streamer rather than the attributes of the commodity.

A Study on the Factors Influencing Consumer Purchase Decision Under the Live-Streaming Sales Model

In recent years,with the rapid development and popularization of Internet information technology,many new media platforms have risen rapidly,and major e-commerce companies have begun to explore the mode of livestreaming.Especially during the COVID-19 pandemic,due to the lockdown,live-streaming has become an important means of economic development in many places.Owing to its remarkable characteristics of timeliness,entertainment,and interactivity,it has become the latest and trendiest sales mode of e-commerce channels,reflecting huge economic potential and commercial value.This article analyzes two models and their characteristics of live-streaming sales from a practical perspective.Based on this,it outlines consumer purchasing decisions and the factors that affect consumer purchasing decisions under the live-streaming sales model.Finally,it discusses targeted suggestions for using the live-streaming sales model to expand the consumer market,hoping to promote the healthy and steady development of the live-streaming sales industry.

Modelling the Roles of Cewebrity Trust and Platform Trust in Consumers’Propensity of Live-Streaming:An Extended TAM Method

Live streaming is a booming industry in China,involving an increasing number of Internet users.Previous studies show that trust is a cornerstone to develop ecommerce.Trust in the streaming industry is different from that of other e-commerce areas.There are two major dimensions of trust in the live streaming context:platform trust and cewebrity trust,which are both important for customers to adopt and reuse a specific live streaming service.We collected questionnaire data from 520 participates who have used live streaming services in China.We model the collected data and identified factors that can influence users’propensity by an extended technology acceptance model(TAM)method.According to our analysis,both cewebrity trust and platform trust will greatly influence users’intention to reuse a certain platform.Moreover,results also indicate that cewebrity trust is far more important than platform trust.These findings can lead to several management strategies to improve the adherence of users to streaming platforms.

Deep Sequential Model for Anchor Recommendation on Live Streaming Platforms

Live streaming has grown rapidly in recent years, attracting increasingly more participation. As the number of online anchors is large, it is difficult for viewers to find the anchors they are interested in. Therefore, a personalized recommendation system is important for live streaming platforms. On live streaming platforms, the viewer’s and anchor’s preferences are dynamically changing over time. How to capture the user’s preference change is extensively studied in the literature, but how to model the viewer’s and anchor’s preference changes and how to learn their representations based on their preference matching are less studied. Taking these issues into consideration, in this paper, we propose a deep sequential model for live streaming recommendation. We develop a component named the multi-head related-unit in the model to capture the preference matching between anchor and viewer and extract related features for their representations. To evaluate the performance of our proposed model, we conduct experiments on real datasets, and the results show that our proposed model outperforms state-of-the-art recommendation models.

Scheduling piece requests blindly and randomly for peer-to-peer live streaming

The design of piece scheduling algorithm is fundamental in mesh-based peer-to-peer （P2P） live streaming systems. This article regards the piece propagation process in P2P live streaming system as a conflict process, where peers compete to get the piece as soon as possible to meet the tight timing requirement of the live system. In this vision, the authors reduce the piece scheduling problem as a conflict resolution problem, and propose a blind random scheduling method to resolve it. The algorithm can be described as： 1） pieces are scheduled independently; 2） conflicts are resolved by the random back off algorithm. The theoretical analysis and simulation results prove that the algorithm scales well with the network size, and can obtain near-optimal in-time delivery of pieces. The algorithm is also easy to implement in the real-world system.

Efficient User Authentication and Key Management for Peer-to-Peer Live Streaming Systems

Recent development of the peer-to-peer （P2P） live streaming technique has brought unprecedented new momentum to the Internet with the characters of effective, scalable, and low cost. However, before these applications can be successfully deployed as commercial applications, efficient access control mechanisms are needed. This work based on earlier research of the secure streaming architecture in Trust- Stream, analyzes how to ensure that only authorized users can access the original media in the P2P live streaming system by adopting a user authentication and key management scheme. The major features of this system include （1） the management server issues each authorized user a unique public key certificate, （2） the one-way hash chain extends the certificate＇s lifetime, （3） the original media is encrypted by the session key and delivered to the communication group, and （4） the session key is periodically updated and distributed with the media. Finally, analyses and test results show that scheme provides a secure, scalable, reliable, and efficient access control solution for P2P live streaming systems.

Effects of age on live streaming viewer engagement:a dual coding perspective

Though the emerging live streaming industry has attracted growing attention,the dominant yanzhi category where streamers mostly interact with the audience through amateur talent shows and casual chats has not been widely investigated.To decode the mechanism behind the popularity of yanzhi streamers,this study draws on Dual Coding Theory(DCT)to posit that age estimated from a streamer’s face and voice can influence the level of viewer engagement.To validate our hypothesized relationships,274 one-minute video records ahead of a viewer commenting or/and gifting were collected and analyzed via deep learning algorithms.Analytical results attest to the negative effects of both facial and vocal age on viewer engagement,while their interaction has a positive impact on viewer engagement.

A Cost-Effective Cloud Leasing Policy for Live Streaming Applications

The dynamics of the globalized multimedia sources and request demands, which requires high computations and bandwidths, makes the IT infrastructure a challenge for live streaming applications. Migrating the system to a geo-distributed cloud and leasing servers is an ideal alternative for supporting large-scale live streaming applications with dynamic contents and demands. The new challenge of multimedia live streaming applications in a geo-distributed cloud is how to efficiently arrange and migrate services among different cloud sites to guarantee the distribute users’ experience at modest costs. This paper first investigates cloud leasing policies for live streaming applications and finds that there is no detailed algorithm to help live streaming applications arrange and migrate services among different cloud sites. Then, we present a quality of service（Qo S） guarantee cost-effective cloud leasing policy for live streaming applications. Meanwhile, we design a genetic algorithm（GA） to deal with the leasing policy among cloud sites of diverse lease prices. Experimental results confirm the effectiveness of the proposed model and the efficiency of the involved GA.

Machine Learning-based Stable P2P IPTV Overlay

Live video streaming is one of the newly emerged services over the Internet that has attracted immense interest of the service providers.Since Internet was not designed for such services during its inception,such a service poses some serious challenges including cost and scalability.Peer-to-Peer(P2P)Internet Protocol Television(IPTV)is an application-level distributed paradigm to offer live video contents.In terms of ease of deployment,it has emerged as a serious alternative to client server,Content Delivery Network(CDN)and IP multicast solutions.Nevertheless,P2P approach has struggled to provide the desired streaming quality due to a number of issues.Stability of peers in a network is one of themajor issues among these.Most of the existing approaches address this issue through older-stable principle.This paper first extensively investigates the older-stable principle to observe its validity in different scenarios.It is observed that the older-stable principle does not hold in several of them.Then,it utilizes machine learning approach to predict the stability of peers.This work evaluates the accuracy of severalmachine learning algorithms over the prediction of stability,where the Gradient Boosting Regressor(GBR)out-performs other algorithms.Finally,this work presents a proof-of-concept simulation to compare the effectiveness of older-stable rule and machine learning-based predictions for the stabilization of the overlay.The results indicate that machine learning-based stability estimation significantly improves the system.

PROSE: Proactive, Selective CDN Participation for P2P Streaming

Many production peer-to-peer （P2P） streaming systems use content delivery networks （CDN） to protect the user＇s quality of experiences. Thus, how to efficiently utilize the capacity of CDN （e.g., which peers receive services from the CDN nodes） is a problem of practical significance. Existing solutions adopt a passive, on-demand approach, which is inefficient in utilizing CDN resources. In this paper, we propose PROSE, a simple, novel scheme to achieve proactive, selective CDN participation for P2P streaming. PROSE introduces novel concepts such as choke point expansion nodes/super nodes and leads to efficient, light-weighted, and distributed algorithms to identify and serve these nodes using CDN. Our experimental results show that PROSE achieves at least 10%~25% performance improvement and 2~4 times overhead reduction compared with existing general CDN-P2P-hybrid schemes.