Streaming Scheme Research Articles

TikTok Live Streaming Strategy for Msmes in Tasikmalaya to Increase Sales

This research aims to analyze and supplement the live streaming strategies that have not yet been implemented at Lalada Food. Additionally, this study evaluates the impact of the closure and reopening of TikTok Shop on live streaming strategies and proposes new strategies to increase sales and address the challenges arising from these changes. This research uses a qualitative approach focusing on in-depth observation. SWOT analysis is used to identify the strengths, weaknesses, opportunities, and threats faced by Lalada Food in conducting live streaming. The company occupies a position in quadrant II of the SWOT analysis, which represents a situation where the company's strengths are greater than its weaknesses, but opportunities are smaller than threats. Therefore, the company is in a strong position but faces significant challenges. The strategy used in this quadrant is the diversification strategy. This research is the first to analyze the implementation of live streaming strategies following the closure and reopening of TikTok Shop. This study provides new insights into their marketing strategies through live streaming to increase sales and address business challenges

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.

Nefis: A network coding based flexible device-to-device video streaming scheme

Wireless device-to-device (D2D) communication has empowered efficient and convenient video sharing among neighboring devices. However, the mobility and diversity of user devices, combined with dynamically shifting channel conditions, make these processes susceptible to environmental interference. In this paper, we introduce an adaptive network coding scheme tailored for D2D raw source video streaming in the scalable video coding (SVC) format. Video frames are partitioned into equal-sized fragments and encoded into streaming packets with the addition of certain redundancy. To address the observed deficiency where decoding throughput of coded video frames is generally lower than that of the encoding operation on mobile device platforms, we introduce a parallel decoding algorithm based on LU decomposition. Furthermore, we propose a flexible adjustment mechanism, named Nefis, which dynamically adapts the network coding redundancy and stream resolution based on the statistical model established to predict current network conditions and video streaming quality. Implementation on Android platform demonstrates that Nefis reduce redundancy in bandwidth usage during streaming process and enhance resilience to network dynamics. Experimental results also conclusively demonstrate the feasibility and advantages of network coding technology in D2D streaming applications. Nevertheless, achieving these advantages requires carefully designed encoding and decoding mechanisms.

JND-Aware Two-Pass Per-Title Encoding Scheme for Adaptive Live Streaming

JND-Aware Two-Pass Per-Title Encoding Scheme for Adaptive Live Streaming

MADRL-Based Joint Edge Caching and Bitrate Selection for Multicategory 360° Video Streaming

360-degree video streaming has gained increasing attraction in the current popular VR, AR, and MR applications, which can provide users with an immersive experience. In tile-based 360-degree video streaming, edge caching and bitrate selection strategies are jointly designed to improve users’ Quality of Experience (QoE), which incorporates video quality and rebuffer. However, the existing QoE-driven approaches use a unified QoE function to guide the decisions of edge caching and bitrate selection, which neglect the impact of video quality and rebuffer on different categories of 360-degree videos, thus failing to provide high average QoE for users. In this paper, we propose a MADRL-based joint edge caching and bitrate selection strategy for multicategory 360-degree video streaming to improve users’ average QoE. The key idea is to employ different edge caching and bitrate selection strategies for different video categories to enable fine-grained performance optimization. Based on multicategory 360-degree video streaming, we first model a joint edge caching and bitrate selection problem as a multi-agent cooperative Markov decision process with the goal of maximizing users’ average QoE. Next, an FoV-aware multi-agent soft actor-critic (FA-MASAC) algorithm is designed to help agents collaboratively learn optimal edge caching and bitrate selection decisions in a distributed way, in which each video category is treated as an agent. Finally, experimental results on real-world datasets show that our proposed strategy can greatly benefit users’ average QoE compared to existing strategies.

QoE Aware Video Streaming Scheme Utilizing GRU-Based Bandwidth Prediction and Adaptive Bitrate Selection for Heterogeneous Mobile Networks

QoE Aware Video Streaming Scheme Utilizing GRU-Based Bandwidth Prediction and Adaptive Bitrate Selection for Heterogeneous Mobile Networks

Pendampingan Penerapan Keterampilan Menulis melalui Strategi Image Streaming pada Pembelajaran Bahasa Siswa Kelas VII SMPN 1 Sanrobone

Looking at the nature of the Image Streaming strategy, it is hoped that the Image Streaming strategy can be used as an effective and appropriate strategy when used in learning to write narratives in the form of short story texts. This service aims to provide continuous and continuous learning so that it is not just a matter of putting together a complete word but can be put into writing using the right strategy; improving students' skills by implementing appropriate learning strategies, namely the image streaming strategy in the learning process so that students can practice expressing their ideas in written form; find out the extent to which students' abilities have increased in writing skills to compose a complete sentence through the Image streaming strategy. In order to achieve this goal, the service team carried out planning by chronologically chronologically lining up its implementation starting from discussions, providing materials, providing assistance and implementing the Image Streaming strategy, as well as evaluating the results of student activities. Therefore, the final result of this community service is that it can provide motivation and useful information for students to be more active in the teaching and learning process in class.

The Factors of Live Streaming Strategies and Sales Performance Between Customer Retention and Shopping Experience: A Study on Hot Springs Hotel in China

The Factors of Live Streaming Strategies and Sales Performance Between Customer Retention and Shopping Experience: A Study on Hot Springs Hotel in China

Live Streaming Strategy on TikTok Shop Media in Increasing Sales of Areta Wholesale Negligee

Live Streaming Strategy on TikTok Shop Media in Increasing Sales of Areta Wholesale Negligee

Thread-safe lattice Boltzmann for high-performance computing on GPUs

We present thread-safe, highly-optimized lattice Boltzmann implementations, specifically aimed at exploiting the high memory bandwidth of GPU-based architectures. At variance with standard approaches to LB coding, the proposed strategy, based on the reconstruction of the post-collision distribution via Hermite projection, enforces data locality and avoids the onset of memory dependencies, which may arise during the propagation step, with no need to resort to more complex streaming strategies. The thread-safe lattice Boltzmann achieves peak performances, both in two and three dimensions and it allows to reduce significantly the memory footprint (tens of GigaBytes for order billions lattice nodes simulations) by retaining the algorithmic simplicity of standard LB computing. Our findings open attractive prospects for high-performance simulations of complex flows on GPU-based architectures.

Strategic live streaming choices for vertically differentiated products

Live streaming as a new shopping channel can disclose revealing information on quality preference. With this channel, viewers can obtain additional product details along with entertainment utility from streamers' product showcasing. This paper examines live streaming strategies for vertically differentiated firms that sell products of high or low quality. We obtain the optimal pricing strategies for the high-quality, low-quality firms under a particular live streaming strategy (i.e., (N,N); (S,N); (N,S); (S,S)). We identify how the competing firms should set their live streaming strategies. As the entertainment value increases, firms with a competitive edge are more willing to adopt live streaming. If the “quality-adjusted cost” is lower than 12, then the high-quality firm is prone to adopt live streaming; otherwise, the low-quality firm is prone to adopt live streaming. The entertainment value greatly influences the adoption decision. When the streamer is more popular and charges a medium fixed cost, the competitive firm (i.e., small marginal cost, high quality) should adopt live streaming to expand its marketplace. The other firm should refuse live streaming and emphasize improving its product quality and competitiveness. Interestingly, live streaming is not always beneficial to firms when the competitive advantages dominate the impact of live streaming and the informative degree is improved little.

Live streaming strategy, corporate social responsibility, and online consumer purchasing behavior: Basis for Integrated marketing strategy module of Chinese e-commerce company

Live streaming strategy, corporate social responsibility, and online consumer purchasing behavior: Basis for Integrated marketing strategy module of Chinese e-commerce company

Flexible HTTP-based Video Adaptive Streaming for good QoE during sudden bandwidth drops

We have observed a boom in video streaming over the Internet, especially during the Covid-19 pandemic, that could exceed the network resource availability. In addition to upgrading the network infrastructure, finding a way to smartly adapt the streaming system to the network and users’ conditions to satisfy clients’ perceptions is exceptionally critical, too. This paper proposes a new QoE-aware adaptive streaming scheme over HTTP - ABRA - to make flexible adaptations based on the network and the client’s current status. Besides, we propose a technique that can keep the buffer at an average high for more than 10s. We were limiting the phenomena of rebuffering due to unexpected and unpredictable bandwidth changes. The algorithm keeps the quality of subsequent versions’ quality constant even when the average bitrate decreases, increasing the QoE. Experimental results show that our method can improve QoE from 7.86% to 20.41% compared to state-of-the-art methods. ABRA can provide better performance in terms of QoE score in all buffer conditions compared to the existing solutions while maintaining a minimum secured buffer level for the worst case.

Livestream Marketing Strategy: Tactics of Chinese Top Influencer Austin Li

This paper analyzes the E-commerce live streaming strategy and team composition of Lijia Qi (Austin), the most influential E-commerce live streaming Influencer in China. Using the SWOT analysis framework, the advantages and disadvantages of the Austin Li team were analyzed in detail. Based on this research, insightful conclusions were gained by in-depth research of the entire Chinese live-streaming E-commerce industry, while giving scientific suggestions for the future development of both the industry and participants. The results show that Austin Li has a structured team composition. Different departments can rapidly cooperate with each other. The team has the professional and powerful insight to support Austin's business by making good backstage support. Austin Li has sensitive policies Insight. He cooperates with the Chinese government on Live streaming and obtains the support of government departments. Austin Li has good empathy and language skills, which can let him quickly infect his fans immediately to further seizes the solid foundation of purchasing power.

ARARAT: A Collaborative Edge-Assisted Framework for HTTP Adaptive Video Streaming

With the ever-increasing demands for high-definition and low-latency video streaming applications, network-assisted video streaming schemes have become a promising complementary solution in the HTTP Adaptive Streaming (HAS) context to improve users’ Quality of Experience (QoE) as well as network utilization. Edge computing is considered one of the leading networking paradigms for designing such systems by providing video processing and caching close to the end-users. Despite the wide usage of this technology, designing network-assisted HAS architectures that support low-latency and high-quality video streaming, including edge collaboration is still a challenge. To address these issues, this article leverages the Software-Defined Networking (SDN), Network Function Virtualization (NFV), and edge computing paradigms to propose A collaboRative edge-Assisted framewoRk for HTTP Adaptive video sTreaming (ARARAT). Aiming at minimizing HAS clients’ serving time and network cost, besides considering available resources and all possible serving actions, we design a multi-layer architecture and formulate the problem as a centralized optimization model executed by the SDN controller. However, to cope with the high time complexity of the centralized model, we introduce three heuristic approaches that produce near-optimal solutions through efficient collaboration between the SDN controller and edge servers. Finally, we implement the ARARAT framework, conduct our experiments on a large-scale cloud-based testbed including 250 HAS players, and compare its effectiveness with state-of-the-art systems within comprehensive scenarios. The experimental results illustrate that the proposed ARARAT methods (i) improve users’ QoE by at least 47%, (ii) decrease the streaming cost, including bandwidth and computational costs, by at least 47%, and (iii) enhance network utilization, by at least 48% compared to state-of-the-art approaches.

HTTP adaptive streaming scheme based on reinforcement learning with edge computing assistance

As the number of users and the types of videos viewed increase, seamless video streaming services are becoming more important. Adaptive streaming aims to achieve high Quality of Experience (QoE) in time-varying network conditions. However, the existing schemes lack considerations for quality adaptation to improve QoE under dynamic network environments and multi-client competition. In this paper, we propose an HTTP adaptive streaming scheme based on reinforcement learning with edge computing assistance. The proposed scheme uses reinforcement learning to generate robust adaptation policy for changes in the network conditions. Edge computing plays a role of managing multiple clients based on information about the network and client. The proposed scheme considers subjective quality, multiple videos, and learning variances to advance the adaptation policy. By balancing between individual QoE and QoE fairness, the proposed scheme enables multiple clients to utilize the bandwidth as much as required. Experimental results show that the proposed scheme has better performance for individual QoE and QoE fairness than the existing schemes.

D2D-assisted user-centric adaptive video transmission in next generation cellular networks

Next-generation cellular networks need to provide seamless connectivity with higher data rates, increased capacity, and enhanced network coverage. As multimedia service demands in various heterogeneous devices grow rapidly compared to the underlying network’s capacity and bandwidth, the adaptation in multimedia streaming services is essential for providing satisfactory Quality of Experience (QoE). This paper develops a Device-to-Device (D2D)-assisted Utility-based Adaptive Multimedia (video) Streaming scheme (UAMS) using D2D communication in a 5th Generation (5G) cellular network where low-battery users may extend their streaming duration by spending lower reception energy with the help of D2D-assisted communication. The adaptation algorithm considers four utility functions: quality, power consumption, packet error ratio, and remaining battery of the user devices to adapt the bitrate dynamically and augment viewers’ experience. We formulate an optimization problem to maximize the joint utility function to provide the best adaptive multimedia content selected for transmission to the end-users either directly or via D2D Relay Nodes (DRNs) in every scheduling interval. We use a graph theoretic approach for choosing the best DRNs. Extensive simulations show the efficacy of the proposed scheme in terms of saved battery energy, churn rate, and QoE metrics compared to a few well-known existing schemes in the literature that do not use D2D communication.

Quality of Experience Oriented Adaptive Video Streaming for Edge Assisted Cellular Networks

HTTP adaptive streaming accounts for a large part of mobile Internet traffic. With the developing cellular communication technologies, the standard dynamic adaptive streaming over HTTP technique allows mobile terminals to adaptively configure the transmission rate of video streaming applications and achieve high quality of experiences (QoE) with many adaptive bitrate algorithms. However, existing schemes either neglect to consider radio access network (RAN) side conditions or only consider simple RAN information optimization schemes. In this letter, we propose a QoE-oriented adaptive video streaming scheme based on a dueling deep Q-learning network. The proposed scheme improves the QoE by jointly considering the physical layer transmission bandwidth and the higher layer buffer status. Through the numerical and prototyping results, we show that our proposed scheme outperforms the existing schemes, with the average QoE improvements of 12.6% to 28.8%.

The Adaptive Streaming SAR Back-Projection Algorithm Based on Half-Precision in GPU

The back-projection (BP) algorithm is completely accurate in the imaging principle, but the computational complexity is extremely high. The single-precision arithmetic used in the traditional graphics processing unit (GPU) acceleration scheme has low throughput and its usage of the video memory is large. An adaptive asynchronous streaming scheme for the BP algorithm based on half-precision is proposed in this study, and then it is extended to the fast back-projection (FBP) algorithm. In this scheme, the adaptive loss factors selection strategy ensures the dynamic range of data, the asynchronous streaming structure ensures the efficiency of large scene imaging, and the mixed-precision data processing ensures the imaging quality. The schemes proposed in this paper are compared with BP, FBP, and fast factorized back-projection (FFBP) algorithms of single-precision in GPU. The experimental results show that the two half-precision acceleration schemes in this paper reduce the video memory usage to 74% and 59% of the single-precision schemes with guaranteed image quality. The efficiency improvements of the proposed schemes are almost one and 0.5 times greater than that of the corresponding single-precision scheme, and the advantage can be more obvious when dealing with large computations.

QAVA: QoE-Aware Adaptive Video Bitrate Aggregation for HTTP Live Streaming Based on Smart Edge Computing

Currently video streaming in heterogeneous network environments is affected by limited network bandwidth availability and consequent low and variable user Quality of Experience (QoE) levels. In particular, for the case of live video streaming, a very high number of end-clients request content at the same time, generating huge concurrent traffic, and putting pressure on the existing network infrastructure. An approach which helps address this issue is deployment of emerging edge computing technologies to smooth the live streaming traffic and improve QoE by adapting client bitrates and caching content at the edge server. In this context, this paper proposes a novel <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</b> oE-aware <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> daptive <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</b> ideo bitrate <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> ggregation scheme for HTTP live streaming based on smart edge computing (QAVA). As an intelligent proxy server, a “smart edge” which deploys QAVA aggregates all the traffic requested by clients for the same live streaming service and adapts their bitrates based on network conditions, client states and video characteristics. The adaptation is performed based on a Deep Reinforcement Learning (DRL)-based algorithm, which is also proposed. The QAVA DRL algorithm is trained and modeled based on a real client experience dataset. The experimental evaluation results presented in this paper show how QAVA outperforms other state-of-the-art adaptive bitrate algorithms in terms of average QoE and QoE fairness.