Video streaming

Nowadays Wireless technologies have wide use of multimedia. In streaming applications, media streams have to be transmitted continuously. It has to overcome the network problems such as delay, jitter, handoff, packet loss, QoS, congestion. In wireless video streaming, buffering part plays an important role. To provide better performance for streaming multimedia over best effort networks, such as the Internet and wireless networks, buffer techniques are often used on the server side, network and on the client side. In this paper, we are going to study the Server side and client side buffers. first we will study the server side buffer which is act as multibuffer. In multibuffer we are studying two schemes scheduling and rate control. Now we will move to client-side buffer. In client side buffer they are having different names for this such as pre-buffer, playback

Video streaming is gaining popularity among mobile users recently. Considering that the mobile devices have limited computational capacity and energy supply, and the wireless channels are highly dynamic, it is very challenging to provide high quality video streaming services for mobile users consistently. To overcome the above disadvantages of progressive download, dynamic adaptive streaming over HTTP (DASH) has been proposed. In a DASH system, multiple copies of pre-compressed videos with different resolution and quality are stored in segments. In this paper, formulate the multi-link video streaming process as a reinforcement learning task. For each streaming step, define a state to describe the current situation, including the index of the requested segment, the current available bandwidth and other system parameters. A finite state Markov Decision Process (MDP) can be modeled for this reinforcement learning task.

Abstract - Video streaming is before quality among mobile users. the newest mobile devices, like good phones and tablets, are well equipped with multiple wireless network devices. so as to continue video streaming quality whereas reducing the wireless service price, during this paper, the foremost favorable video streaming method with multiple links is developed as a Andrei Markov Decision Process (MDP). This perform is intended to boost the Quality of service (QoS) needs for video traffic, like the startup Latency, playback fluency, average playback quality, playback smoothness and wireless service price. To estimate the performance of the planned adaptation algorithmic rule, we tend to enforced a work victimization the humanoid movable and therefore the Scalable Video Coding(SVC)codec. The planned system provides associate economical interactive streaming service for varied mobile devices and dynamic network environment for video Streaming over multiple wireless access networks.

The scope of this project covers mainly on two parts; the first is the establishment of wireless mesh network using Mikrotik Routerboard 433UAH. Three boards are used to establish mesh network each with the same channel as WMN demands it. This is followed by the successful internet connection to enable internet access for mesh clients. Another primary scope of this project is the analysis of video streaming quality in WMN from server to client by sending video script. Analysis is done using software IXChariot tool that checks the network level quality of service parameters. Those parameters that will be analyzed are delay, jitter, data loss, and throughput and media loss rate. The secondary scope of this project is based on the analysis summarizing the quality if video streaming and the element that affects the quality of video streaming.

Focal of this development is to provide a mobile Application using video streaming over BT network application with client-server architecture. General Application Android Wear technology is tailored made specifically for Google devices like Watch, Glasses. It comes with a new simplified interface with collection of fitness features. Smart watch has advanced health tracker application, it coaches and remind about workouts, speed, distance, and time information. Biometric tools/sensor in watch integrated with fitness Application. It monitors Heart rate and update average Heart Beat information in regular basis. The Android Wearable Device act as Client able to stream live camera content to Android Handheld Device like Mobile Phone/Tablet. Similar way Camera content from Mobile Phone/table to your watch. Bluetooth is wireless communication layer to transfer media content between the devices. The sensor information send to server along with media content information will provide more clarity on individual health conscious and tracking their fitness for extensive life cycle.

On a typical video streaming website, videos can have a variety of forms of content, but generally the longer videos can be, e.g., movies, TV show episodes, a compilation of shorter videos or songs, or a stream of a video game. Other sources of video content can be used with the system of the present disclosure. For example, the video could be provided as input, or could be processed beforehand. For instance, the visual portion of the video could be sampled into image frames, the audio portion could be decomposed into channels, or other suitable pre- processing could be performed on the video. In some embodiments, the video streaming website can know the content type of the video. For example, the video streaming website may prompt the video’s uploader to give the content type, such as by providing a prompt asking the video’s uploader whether the video is a music playlist, compilation video, movie, episode of a TV show, stream of a video game, or other.

Many open problems remain, particularly in the context of wireless mesh networks. For instance, it is still unclear whether the stringent latency constraint (usually less than a second) for video streaming can be met when packets need to be delivered over multiple hops of time-varying wireless links in a mesh network. Conditions where multipath routing is beneficial for streaming need to be identified, as contention of video traffic along parallel paths may cancel out the path diversity advantage of robustness to packet losses. Typically the wireless network is shared by both video streaming and other applications such as file downloading. The problem re- mains to be addressed as how to optimally allocate network resource among heterogeneous traffic types, each bearing a different performance metric (e.g., completion time for file downloading versus video quality for streaming).

In [2] the buffer management and congestion control presented in [1] are applied to video streaming. Also in this case the buffer is implemented at the source and it queues packets from the encoder and dequeued packets are transmitted using a randomized binomial scheme. The buffer algorithm is the same as the one presented in [1]. However, differently from the previous work, the Round Trip Times (RTT) are used to estimate the drop probability of a packet. An integrated video communication scheme for stored variable bit-rate (VBR) video streaming in a congested network is presented in [23]. This scheme regulates the transmission rate through a refined rate control algorithm based on the Program Clock References (PCR) value embedded in the video streams. Furthermore, multiple buffers for different importance levels, along with an intelligent selective frame discard algorithm are applied at the source. An architecture which includes an input buffer at the server coupled with the congestion control scheme of TCP at the transport layer is presented in [9]. This work assumes that the available bandwidth is sufficient to deliver the high priority frames and the goal is to maximize the number of transported low priority frames subject to the constraint that the loss rate for the high priority frames would be minimal.

In this category we explain the AMES-Cloud framework which includes the Adaptive Mobile Video streaming(AMoV) and the Efficient Social Video sharing ESoV). From the whole video storing and video streaming system in the cloud is called as Video Cloud (VC). In this VC, there is a large-scale video base (VB), which is capable for storing the most of the popular video clips for the video service providers (VSPs). A temporal video base (tempVB) is used to cache new members for the popular videos, while tempVB counts the access frequency of each and every video. The VC keep maintains running a collector to seek videos which are already popular in VSPs, and will re-encode the collected videos into SVC format and store into tempVB first. By this two tier storage, the AMES-Cloud can keep serving most of popular videos eternally .Note that management work will be controlled by the controller in the VC.

Abstract—Current-generation Internet-scale applications, such as video streaming, incur a large amount of wide area traffic. Trans- mitted on the unreliable Internet with no bandwidth guarantees, such traffic is suffering unpredictable network performance, which is however unappealing to the application providers. Fortunately, Internet giants like Google and Microsoft are increasingly deploying their private wide area networks (WANs) to connect their datacenters on the globe. Such private WANs are highly reliable, and can provide predictable network performance. In this paper, we propose a new type of service—inter-datacenter network as a service (iDaaS), where application providers, make bandwidth reservations for bandwidth guarantees from those Internet giants to support their wide area traffic. We study a bandwidth trading market of multiple iDaaS providers and application providers, and concentrate on the essential bandwidth pricing problem. The challenging issue involves in pricing in such a market is that the benefits of both iDaaS providers and application providers are close-knit connected. To address this issue, we model the interaction between iDaaS providers and application providers as a Stackelberg game, and analyze the existence and uniqueness of the equilibrium. We further present an efficient bandwidth price computation algorithm by blending the advantage of a geometrical Nash bargaining solution and the demand segmentation method. Based on the computed price, we present two bandwidth reservation algorithms, where each iDaaS provider’s bandwidth are reserved in a weighted fair manner and a max-min fair manner, respectively. Finally, we conduct comprehensive trace-driven experiments. The evaluation results show the efficiency of our proposed algorithms in the bandwidth market.

Abstract— While demands on video traffic above mobile webs have been souring, the wireless link capacity cannot retain up alongside the traffic demand. The gap amid the traffic demand and the link capacity, alongside alongside time-varying link conditions, aftermath in poor ability quality of video streaming above mobile webs such as long buffering period and intermittent disruptions. Leveraging the cloud computing knowledge, we counsel a new mobile video streaming framework, dubbed AMES-Cloud, that has two main parts: AMoV (adaptive mobile video streaming) and ESoV (efficient communal video sharing). AMoV and ESoV craft a confidential agent to furnish video streaming services effectually for every single mobile user. For a given user, AMoV lets her confidential agent adaptively adjust her streaming flow alongside a scalable video coding method established on the feedback of link quality. Likewise, ESoV monitors the communal web contact amid mobile users, and their confidential agents endeavor to prefetch video content in advance. We apply a prototype of the AMES-Cloud framework to clarify its performance. It is shown that the private

When both bandwidth and energy are severely limited for video streaming, sending a video sequence over with severe distortion is not desirable. Instead, we consider joint video summarization and transmission approaches to achieve the required energy e ffi ciency. Video summarization is a video adaptation technique that selects a subset of video frames from the original video sequence based on some criterion, e.g., some newly defined frame loss distortion metric [12], specified by the user. It generates a shorter yet visually more pleasing sequence than traditional technologies that usually focus on the optimization of quantization parameters (QP) [12], which can have serious artifacts at reconstruction at very low bit rates.

Concerning the first topic, several transport protocols de- signed for video streaming have been proposed, such as the TCP Friendly Rate Control (TFRC) [7], Real Time Stream- ing Protocol (RTSP) [14], Microsoft Media Services (MMS), Real Time Messaging Protocol (RTMP) [3]. Some of the mentioned protocols have been employed in commercial prod- ucts such as RealNetworks, Windows Media Player, Flash Player. Even though TCP has been regarded in the past as inappropriate for the transport of video streaming proto- cols, recently it is getting a wider acceptance and it is being used with the HTTP. This is mainly due to the following reasons: i) Internet applications are rapidly converging on web browsers; ii) HTTP-based streaming is cheaper to de- ploy since it employs standard HTTP servers [17]; iii) TCP has built-in NAT traversal functionalities; iv) it is easy to be deployed within Content Delivery Networks (CDN) [17]; v) TCP delivers most part of the Internet traffic and it is able to guarantee the stability of the network by means of an efficient congestion control algorithm [15].

We have also considered other solutions to rate limit video streaming. A similar idea that requires no ker- nel TCP change is to set the TCP send socket buffer size [19]. In the case of YouTube, the ustreamer TCP send buffer remains auto-tuned [20] during the startup phase in order to send data as fast as possible. Upon entering the throttling phase, the buffer usually is al- ready larger than the intended clamp value. Setting a new send buffer size is not effective until the buffered amount drops below the new size, making it difficult to imple- ment the throttling. Some previous work control the rate by dynamically adjusting the TCP receive window at the receiver or the gateway [15, 16, 21]. Instead, Trickle is server-based making it easier to deploy in a CDN. An- other approach is TCP pacing [23], i.e., pacing cwnd amount of data over the RTT. While this may be the best TCP solution to suppress bursts, it is also more complex to implement. Moreover, studies have shown that Inter- net paths can absorb small amount of packet bursts [7,9]. Our goal is to reduce large burst drops caused by disrup- tions to the TCP self clocking. It is not to eliminate any possible burst completely.

The proposed system ensures that there is an effective bandwidth utilization during Multimedia video streaming and helps the end user with unnecessary data wastage. Since the propose system is based on time based buffer restriction there is no degradation in video quality because no bits are reduced in the video. The proposed system is highly effective in High speed internet bandwidths and also fights against the fair usage policies resulting a cost effective video streaming in terms of data.

Based on Figure 1, to complete the system for the multiple video streaming, the video recording must first be analyzed. After that, the system must able to generate reports to be read by the person responsible for the monitoring such as production line supervisor. This is the basic concept of the monitoring system. There are various ways to do the video analysis. In this paper, we will discuss three techniques. First, by using “on-the-shelf” software, the second is by combining the “on-the-shelf” software and self-programming, and the third one is by developing own programming.

environment. While streaming video data user feels live experience by implementing the layers of base, enhanced in sub video clouds. Data are in the form of encoded and adaptable nature. Finally we focus cost effective data streaming with large scale networks in cloud in future means that optimal pricing in data streaming for mobile users. In this paper, we discussed our proposal of an adaptive mobile video streaming and sharing framework, called AMES-Cloud, which efficiently stores videos in the clouds (VC), and utilizes cloud computing to construct private agent (subVC) for each mobile user to try to offer “non-terminating” video streaming adapting to the fluctuation of link quality based on the Scalable Video Coding technique. Also AMES-Cloud can further seek to provide “nonbuffering” experience of video streaming by background pushing functions among the VB, subVBs and localVB of mobile users.

The mobile application subsystem is divided up into a three layered architecture; it has a user interface, application, and device layer. Each layer has its own interface that other layers can use to interact with it. The user interface layer contains an observer object and updates its data, using data from the observable application layer, via the observer pattern. The application layer handles threads and messages from the user interface layer messages send them to the device layer. The device layer handles the interactions with the hardware, all the features of the phone necessary for the application, including but sending video streaming over wifi, and ports to send and receive data to and from the other Android phone. Interface layer will handle Video encoder/decoder module.

THE explosive advances o f m u l t i m e d i a p r o c e s s i n g t e c h n o l o g i e s a r e creating dramatic shifts in ways that video content is delivered to and consumed by end users. Also, the increased popularity of wireless networks and mobile devices is drawing lots of attentions on ubiquitous multimedia access in the multimedia community in the past decade. Network service providers and researchers are focusing on developing efficient solutions to ubiquitous access of multimedia data, particularly videos, from everywhere using mobile devices (laptops, personal digital assistants, or smart phones that can access 3G networks). Mobile-phone users can watch video programs on their devices by subscribing to the data plans from network service providers], and they can easily use their programmable hand devices to retrieve and reproduce the video content. To accommodate heterogeneous network conditions and devices, scalable video coding is also widely used in mobile video streaming Video applications over mobile devices have drawn lots of attentions in the research community, such as quality measure and error control. There is also a rich body of literature on user interactions in electronic commerce in wireless networks such as cooperative content caching in wireless ad hoc net- work and secure transactions . Therefore, it is important to understand end users’ possible actions in order to provide better ubiquitous video access services.

The distribution of digitized video data, whether on-demand or streaming, is rising tremendously among Internet users in Malaysia especially in areas such as education, communication and television broadcasting. Based on this progression, a research has been conducted to study the relationship between network performance and the quality of video data streamed in FTMM, KUiTTHO local area network. Different types of content and category of video data are streamed to the network which is equipped with LAN 100Base T technology. To achieve the objective, experimental approach is applied to different types of video content namely lecture clips, movie clips and music clips. The quality of transmitted video data is very much depended on two main factors which are packet delay and packet loss. Based on the experiment that has been conducted, packet delay is caused by network traffic condition, video content, LAN architecture, hardware and transmission medium used. In addition to this, the experiment also has shown that packet loss is caused by data burst. This situation happened when packet queue is overloading the router. This research confirms that video streaming quality can be upgraded with the use of better communication medium such as fiber optic, the exploitation of up-to-date network hardware, the utilization of bigger capacity hard disk and finally with the use of smaller window scale in displaying the video content.