Adaptional Algorithmic Rule for Video Streaming

Nowadays major wants of top quality videos while not reproduce interruption. Considering on various request at the same time, it was delaying videos of upper bitrates. During this paper, the load balancing video streaming method we tend to thought of as reinforcement learning task. We tend to outline a state to explain this state of affairs, as well as the index of the requested phase, this offered information measure and alternative system parameters for every streaming step. For this reinforcement learning task a finite state Andre Markov Decision Process (MDP) may be sculptured. To specialize in the problems connected with video streaming is that the psychological feature work of this analysis. On load equalization on server, less buffering, individuals have less time to buffering video and streaming depends. However, with the introduction of two internet server system performance is seems to be increased

Quality of experience and HTTP adaptive streaming: a review of subjective studies

HTTP adaptive streaming technology has become widely spread in multimedia services because of its ability to provide adaptation to characteristics of various viewing devices and dynamic network conditions. There are various studies targeting the optimization of adaptation strategy. However, in order to provide an optimal viewing experience to the end-user, it is crucial to get knowledge about the Quality of Experience (QoE) of different adaptation schemes. This paper overviews the state of the art concerning subjective evaluation of adaptive streaming QoE and highlights the challenges and open research questions related to QoE assessment

Automated QoE Evaluation of Dynamic Adaptive Streaming over HTTP

Dynamic Adaptive Streaming over HTTP (DASH) is referred to as a multimedia streaming standard to deliver high quality multimedia content over the Internet using conventional HTTP Web servers. As a fundamental feature, it enables automatic switching of quality levels according to network conditions, user requirements, and expectations. Currently, the proposed adaptation schemes for HTTP streaming mostly rely on throughput measurements and/or buffer-related metrics, such as buffer exhaustion and levels. In this paper, we propose to enhance the DASH adaptation logic by feeding it with additional information from our evaluation of the users' perception approximating the userperceived quality of video playback. The proposed model aims at conveniently combining TCP-, buffer-, and media content-related metrics as well as user requirements and expectations to be used as an input for the DASH adaptation logic. Experiments have demonstrated that the chosen model enhances the capability of the adaptation logic to select the optimal video quality level. Finally, we integrated all our findings into a real DASH system with QoE monitoring capabilitie

On the Bitrate Adaptation of Shared Media Experience Services

In Shared Media Experience Services (SMESs), a group of people is interested in streaming consumption in a synchronised way, like in the case of cloud gaming, live streaming, and interactive social applications. However, group synchronisation comes at the expense of other Quality of Experience (QoE) factors due to both the dynamic and diverse network conditions that each group member experiences. Someone might wonder if there is a way to keep a group synchronised while maintaining the highest possible QoE for each one of its members. In this work, at first we create a Quality Assessment Framework capable of evaluating different SMESs improvement approaches with respect to traditional metrics like media bitrate quality, playback disruption, and end user desynchronisation. Secondly, we focus on the bitrate adaptation for improving the QoE of SMESs, as an incrementally deployable end user triggered approach, and we formulate the problem in the context of Adaptive Real Time Dynamic Programming (ARTDP). Finally, we develop and apply a simple QoE aware bitrate adaptation mechanism that we compare against youtube live-streaming traces to find that it improves the youtube performance by more than 30%

Fair-RTT-DAS: A robust and efficient dynamic adaptive streaming over ICN

To sustain the adequate bandwidth demands over rapidly growing multimedia traffic and considering the effectiveness of Information-Centric Networking (ICN), recently, HTTP based Dynamic Adaptive Streaming (DASH) has been introduced over ICN, which significantly increases the network bandwidth utilisation. However, we identified that the inherent features of ICN also causes new vulnerabilities in the network. In this paper, we first propose a novel attack called as Bitrate Oscillation Attack (BOA), which exploits fundamental ICN characteristics: in-network caching and interest aggregation, to disrupt DASH functionality. In particular, the proposed attack forces the bitrate and resolution of video received by the attacked client to oscillate with high frequency and high amplitude during the streaming process. To detect and mitigate BOA, we design and implement a reactive countermeasure called Fair-RTT-DAS. Our solution ensures efficient bandwidth utilisation and improves the user perceived Quality of Experience (QoE) in the presence of varying content source locations. For this purpose, Fair-RTT-DAS consider DASH\u2019s two significant features: round-trip-time (RTT) and throughput fairness. In the presence of BOA in a network, our simulation results show an increase in the annoyance factor in user\u2019s spatial dimension, i.e., increase in oscillation frequency and amplitude. The results also show that our countermeasure significantly alleviates these adverse effects and makes dynamic adaptive streaming friendly to ICN\u2019s implicit features

Adaptive Media Streaming to Mobile Devices: Challenges, Enhancements, and Recommendations

Video streaming is predicted to become the dominating traffic in mobile broadband networks. At the same time, adaptive HTTP streaming is developing into the preferred way of streaming media over the Internet. In this paper, we evaluate how different components of a streaming system can be optimized when serving content to mobile devices in particular. We first analyze the media traffic from a Norwegian network and media provider. Based on our findings, we outline benefits and challenges for HTTP streaming, on the sender and the receiver side, and we investigate how HTTP-based streaming affects server performance. Furthermore, we discuss various aspects of efficient coding of the video segments from both performance and user perception point of view. The final part of the paper studies efficient adaptation and delivery to mobile devices over wireless networks. We experimentally evaluate and improve adaptation strategies, multilink solutions, and bandwidth prediction techniques. Based on the results from our evaluations, we make recommendations for how an adaptive streaming system should handle mobile devices. Small changes, or simple awareness of how users perceive quality, can often have large effects

Subjective and Objective Quality-of-Experience of Adaptive Video Streaming

With the rapid growth of streaming media applications, there has been a strong demand of Quality-of-Experience (QoE) measurement and QoE-driven video delivery technologies. While the new worldwide standard dynamic adaptive streaming over hypertext transfer protocol (DASH) provides an inter-operable solution to overcome the volatile network conditions, its complex characteristic brings new challenges to the objective video QoE measurement models. How streaming activities such as stalling and bitrate switching events affect QoE is still an open question, and is hardly taken into consideration in the traditionally QoE models. More importantly, with an increasing number of objective QoE models proposed, it is important to evaluate the performance of these algorithms in a comparative setting and analyze the strengths and weaknesses of these methods. In this study, we build two subject-rated streaming video databases. The progressive streaming video database is dedicated to investigate the human responses to the combined effect of video compression, initial buffering, and stalling. The adaptive streaming video database is designed to evaluate the performance of adaptive bitrate streaming algorithms and objective QoE models. We also provide useful insights on the improvement of adaptive bitrate streaming algorithms. Furthermore, we propose a novel QoE prediction approach to account for the instantaneous quality degradation due to perceptual video presentation impairment, the playback stalling events, and the instantaneous interactions between them. Twelve QoE algorithms from four categories including signal fidelity-based, network QoS-based, application QoS-based, and hybrid QoE models are assessed in terms of correlation with human perception on the two streaming video databases. Experimental results show that the proposed model is in close agreement with subjective opinions and significantly outperforms traditional QoE models