A topology-aware P2P video streaming system

Research on the use of software agents in P2P video streaming systems has recently emerged in order to meet the requirements such as time constraints, minimizing delay and efficient utilization of network resources. However parent selection and network awareness are not addressed properly in these leading studies. In order to fill this gap, we introduce the design and implementation of a Multi-agent System (MAS) for P2P video streaming in this paper. The construction of the MAS by applying a well-defined agent-oriented software engineering methodology is discussed. The internal structure of each agent in the proposed MAS is based on the well-known Belief-Desire-Intention (BDI) architecture. Experimental results show that the proposed MAS succeeds the related parent selection and promotes the system execution. BDI-based MAS design given in this paper can guide those researchers who aim to implement autonomous mesh-based P2P video streaming systems.

Recently, video streaming services using P2P(Peer-to-Peer) have attracted attention to solve the problem of load concentration on servers and reduce a lot of latency. Many P2P streaming systems, like Coolstreaming, however, have a complicated approach to control playback timings severely. And this leads to less churn resiliency and less adaptability to fluctuation of network traffic. Therefore, we focus on a simple and robust approach to realize “pseudo” streaming with high quality, which is based on BitTorrent. In the existing methods with the simple approach, peers download pieces just closer to playback timings to decrease the playback discontinuity. In contrast, in this paper, we propose a new P2P video streaming system considering the cooperation of three metrics; video structure, playback timings, and pieces dispersion on network. In this system, users vary three piece selections to suit the delivery status. Specifically, users preferentially download pieces which affect the video quality, are closer to playback timings, and improve the delivery efficiency. Moreover, we show the effectiveness of the proposed method by computer simulation.

Video streams, either in form of on-demand streaming or live streaming, usually have to be converted (i.e., transcoded) based on the characteristics of clients' devices (e.g., spatial resolution, network bandwidth, and supported formats). Transcoding is a computationally expensive and time-consuming operation, therefore, streaming service providers currently store numerous transcoded versions of the same video to serve different types of client devices. Due to the expense of maintaining and upgrading storage and computing infrastructures, many streaming service providers (e.g., Netflix) recently are becoming reliant on cloud services. However, the challenge in utilizing cloud services for video transcoding is how to deploy cloud resources in a cost-efficient manner without any major impact on the quality of video streams. To address this challenge, in this paper, we present the Cloud-based Video Streaming Service (CVSS) architecture to transcode video streams in an on-demand manner. The architecture provides a platform for streaming service providers to utilize cloud resources in a cost-efficient manner and with respect to the Quality of Service (QoS) demands of video streams. In particular, the architecture includes a QoS-aware scheduling method to efficiently map video streams to cloud resources, and a cost-aware dynamic (i.e., elastic) resource provisioning policy that adapts the resource acquisition with respect to the video streaming QoS demands. Simulation results based on realistic cloud traces and with various workload conditions, demonstrate that the CVSS architecture can satisfy video streaming QoS demands and reduces the incurred cost of stream providers up to 70%.

Video streaming over the internet for new 3D immersive media such as Virtual Reality and 360-degree videos has drawn great attention from both consumers and researchers in recent years. One of the biggest challenges in streaming such 3D media is the high bandwidth demands. While traditional 2D video streaming is still dominating network peak traffic, new inventions are accelerating the adoption of immersive contents and devices. A new Tile-based video is introduced in both a video codec and streaming layer to reduce the transferred media size. Dynamic adaptive streaming over HTTP has become one of the de facto effective adaptive streaming approaches that can fully utilize the existing physical IP network infrastructure. In this paper, we propose a tile-based streaming framework using software-defined networking. By prioritizing streaming flows, based on the region of interests, our approach can improve the user's quality of experience (QoE).

Advantages of Software Defined Networks (SDN) technology, which decouples control and transmission planesof computer networks, provides the infrastructure that can help to improve the system performance of video streaming applications, one of today's popular Internet applications. The SDN controller may assist the clients by using the network statistics while determining the routes of the appropriate streaming paths in the physical network. In this study, a layered video streaming architecture is proposed, in which SDN controller recommends a server, video codec, and the optimal number of video layers to clients, with taking into account the server load and network status. Performance of the proposed system is given comparatively and it has been shown that the system parameters considered can help to improve the video streaming system performance.

In last few years there has been significant growth in the area of wireless communication. Quality of Service (QoS) has become an important consideration for supporting variety of applications that utilize the network resources. These applications include voice over IP, multimedia services, like, video streaming, video conferencing etc. IEEE 802.16/WiMAX is a new network which is designed with quality of service in mind. This paper focuses on analysis of quality of service as implemented by the WiMAX networks. First, it presents the details of the quality of service architecture in WiMAX network. In the analysis, a WiMAX module developed based on popular network simulator ns-2, is used. Various real life scenarios like voice call, video streaming are setup in the simulation environment. Parameters that indicate quality of service, such as, throughput, packet loss, average jitter and average delay, are analyzed for different types of service flows as defined in WiMAX. Results indicate that better quality of service is achieved by using service flows designed for specific applications.

In Peer-to-Peer (P2P) file sharing systems, such as BitTorrent, peers measure contributions of other peers and upload packets only to a selected subset of peers based on their past contributions. This built-in incentive scheme has alleviated free-riding problem for P2P file sharing. However, it cannot be easily adapted to P2P streaming of scalable videos, because it assumes that each packet is equally important for every peer. This assumption is not true: the importance of a packet to a playback peer depends on a number of factors, including which video layer the packet belongs to, how close it is to the playback deadline, and how many neighboring peers hold this packet. In this paper, we formulated peer selection and packet scheduling process as an integer programming problem. The objective is to maximize the aggregate gain of the packets, measured by their “importance” perceived by playback peers. We evaluated the proposed optimal scheduling algorithm by conducting the experiments in a global scale Internet testbed, PlanetLab, and demonstrated its excellent performance.KeywordsPeer-to-peer networkslayered video codingincentives

Introduction and penetration of high-speed and innovative mobile data services including social media and video services have been increasing significantly across the globe. This growth is driven by deployment and availability of capable and affordable mobile networks and smartphones. These trends have also been observed in Ethiopia as the country’s sole incumbent operator ethio telecom has expanded coverage and capacity of its mobile data network, especially for universal mobile telecommunications system (UMTS) network. The successful provision of these innovative services requires mobile operators to have continuous monitoring of the quality of their networks and customer satisfaction. To that end, this monitoring allows the operator to make timely network optimization, marketing and other relevant decisions that enhance service quality. Traditionally measurement and analysis of network quality of service (QoS) is performed using data from network management system (NMS) and drive/walk test (DT/WT). In recent times, the usage QoS data from crowdsourcing (CS) is also emerging as useful monitoring tool. However, all these methods do not fully capture users’ quality of experience (QoE), which is a function of not only network QoS but also other factors including subjective users’ technical literacy and opinion. As a result, operators have recently provided attention not only for QoS but also QoE. Yet, most quality related analyses are performed using data from only subjective survey, NMS, DT/WT, or CS. It is not straightforward to find quality investigation based on data from all the methods. More importantly, such quality investigation has not been made in the context of Ethiopia’s mobile market. In this paper, we perform QoS and QoE evaluation for UMTS mobile data service based on quality data from all NMS, WT, CS and subjective survey for a business area in Addis Ababa. The evaluation is made using downlink user throughput and mean opinion score (MOS) metrics. The obtained 50th percentile throughput results from NMS, WT and CS are good enough for web browsing and offline video streaming but not for high definition (HD) video calls and live HD video streaming. Furthermore, CS provides optimistic results compared to NMS and WT and its 90th percentile throughput is very good even for live HD video streaming. Moreover, the obtained MOS results from subjective survey almost fit to the QoS results, thus indicating that the network QoS is a significant contributor to users’ perceived QoE.

In this paper, we address the problem of QoS (Quality of Service) provisioning for multi-user video streaming over multiple heterogeneous wireless networks based on the distributed, cross-layer design framework. By jointly considering the rate allocation and the Joint Source-Channel Coding (JSCC), our proposal aims at maximizing the QoS provisioning under the given resource constraint. At first, we develop and evaluate a framework for optimal video rate allocation over multiple networks based on the observed Available Bit Rate (ABR) and the Round Trip Time (RTT) over each access network, as well as the video rate-distortion characteristics. The rate allocation is formulated as a convex optimization problem that minimizes the sum of all video streams expected distortion. Then, we propose an analytical JSCC scheme for error-resilient scalable encoded video, and integrate the JSCC with the specific rate allocation algorithm to improve the constructed video quality by optimally applying the appropriate channel coding rate given the constraints imposed by the transmission rate and the prevailing channel conditions. Objective and subjective simulation results are provided which demonstrate the effectiveness of our proposed joint scheme.

Video streaming for heterogeneous types of devices, where nodes have different devices characteristics in terms of computational capacity and display, is usually handled by encoding the video with different qualities. This is not well suited for Peer-To-Peer (P2P) systems, as a single peer group can only share content of the same quality, thus limiting the peer group size and efficiency. To address this problem, several existing works propose the use of Multiple Descriptions Coding (MDC). The concept of this type of video codec is to split a video in a number of descriptions which can be used on their own, or aggregated to improve the global quality of the video. Unfortunately existing MDC codes are not flexible, as the video is split in a defined number of descriptions. In this paper, we focus on the practical feasibility of using a Fountain MDC code with properties similar to existing Fountain erasure codes, including the ability to create any number of descriptions when needed (on the fly). We perform simulations using selected pictures to assess the feasibility of using these codes, knowing that they should improve the availability of the video pieces in a P2P system and hence the video streaming quality. We observe that, although this idea seems promising, the evaluated benefits, demonstrated by the PSNR values, are limited when used in a real P2P video streaming system.

The multi-path transmission techniques enable multiple paths to maximize resource usage and increase throughput in transmission, which have been installed over mobile devices in recent years. For video streaming applications, compared to the single-path transmission, the multi-path techniques can establish multiple subflows simultaneously to extend the available bandwidth for streaming high-quality videos in mobile devices. Existing adaptive video streaming systems have difficulty in harnessing multi-path scheduling and balancing the tradeoff between the quality of experience (QoE) and quality of service (QoS) concerns. In this paper, we propose an actor-critic network based on Periodical Experience Replay for Multi-path video streaming (PERM). Specifically, PERM employs two actor modules and a critic module: the two actor modules respectively assign the path usage of each subflow and select bitrates for the next chunk of the video, while the critic module predicts the overall objectives. We conduct trace-driven emulation and real-world testbed experiment to examine the performance of PERM, and results show that PERM outperforms state-of-the-art multi-path and single path streaming systems, with an improvement of 10%- 15% on the QoE and QoS metrics.

Video streaming is one of the drastically growing applications and more researchers are focused on their researchers in same. All over the world, the cost of video streaming process is increased up to 37$ billion. Even though video streaming process meets various problems like time, Bit Rate Error (BER) and buffers usage with cost. To provide solution to the above issues, this paper proposed an integrated framework to provide a video streaming method by increased Quality of Service (QoS). The proposed framework integrates (Integrated Framework IF) a popular standard named H.264/AVC video coding and efficient QoE prediction on similar frames in the video. Using the framework, a QoS based video streaming method is proposed, whereas the packet loss is reduced significantly including extra BER value for the channel coding. From the simulation results it is illustrated that high flexibility and efficacy of the proposed framework is more effective in terms of preventing from frequent loss of frames.

Video streams, either in form of on-demand streaming or live streaming, usually have to be converted (i.e., transcoded) based on the characteristics (e.g., spatial resolution) of clients' devices. Transcoding is a computationally expensive operation, therefore, streaming service providers currently store numerous transcoded versions of the same video to serve different types of client devices. However, recent studies show that accessing video streams have a long tail distribution. That is, there are few popular videos that are frequently accessed while the majority of them are accessed infrequently. The idea we propose in this research is to transcode the infrequently accessed videos in a on-demand (i.e., lazy) manner. Due to the cost of maintaining infrastructure, streaming service providers (e.g., Netflix) are commonly using cloud services. However, the challenge in utilizing cloud services for video transcoding is how to deploy cloud resources in a cost-efficient manner without any major impact on the quality of video streams. To address the challenge, in this research, we present an architecture for on-demand transcoding of video streams. The architecture provides a platform for streaming service providers to utilize cloud resources in a cost-efficient manner and with respect to the Quality of Service (QoS) requirements of video streams. In particular, the architecture includes a QoS-aware scheduling component to efficiently map video streams to cloud resources, and a cost-efficient dynamic (i.e., elastic) resource provisioning policy that adapts the resource acquisition with respect to the video streaming QoS requirements.

Ultra-High-Definition (UHD) videos have been getting increasing attention. However, existing video streaming solutions fail to deliver them due to the extremely high bandwidth requirement. The emerging cloud native 5G networks have opened up the possibility of enhancing UHD video quality by leveraging in-network video streaming. Unfortunately, the restricted storage and bandwidth of in-network servers could become the main bottleneck. To this end, we present <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf EMS}$</tex-math></inline-formula> , a novel UHD video streaming framework, by integrating <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</u> rasure-coded storage with <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</u> ulti-source <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> treaming. We respectively introduce a deadline-aware and a latency-sensitive metric to indicate the service quality of video servers and advocate a federated learning paradigm for the adaptive service quality update, including a reinforcement learning based multi-server selection (i.e., user local training) and a global service quality aggregation. To facilitate user local training without sacrificing streaming Quality-of-Experience (QoE), we cast the multi-server selection associated with the restriction on the average number of selected servers per video chunk into two kinds of Multi-Armed Bandit (MAB) models in terms of the proposed service quality metrics. We design lightweight Upper Confidence Bound (UCB) based algorithms with a theoretical performance guarantee. We implement a prototype of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf EMS}$</tex-math></inline-formula> , and extensive experiments confirm the superiority of the proposed algorithms.

Providing quality of service (QoS) for real-time multimedia applications such as video streaming in mobile ad hoc networks (MANETs) is an important challenge. MANETs are characterized by lack of fixed infrastructure, dynamic topology, and limited resources that make more difficult multimedia applications transport and run on this networks. To overcome this challenge, video coding techniques combined to multiple routing paths (multipath) is a promising technique for supporting transmission of multiple video streams with appropriate QoS over mobile ad hoc networks. In this paper, firstly, many issues and different techniques for video streaming over MANET have been reviewed and secondly two multi paths routing protocols (M-AODV and MDSDV) have been evaluated in order to improve QoS for real-time multimedia applications. Results show that none of these two protocols is better than the other. In certain situations (throughput and load network with high mobility) is M-AODV but in others (network load and reliability for large-scale network) is MDSDV protocol which displays good performance. It is also noted that these two protocols provide between acceptable and good quality and a small jitter regardless of nodes number in medium mobility.