Video Streaming In Networks Research Articles

Developing a Video Buffer Framework for Video Streaming in Cellular Networks

This work proposes a new video buffer framework (VBF) to acquire a favorable quality of experience (QoE) for video streaming in cellular networks. The proposed framework consists of three main parts: client selection algorithm, categorization method, and distribution mechanism. The client selection algorithm was named independent client selection algorithm (ICSA), which is proposed to select the best clients who have less interfering effects on video quality and recognize the clients’ urgency based on buffer occupancy level. In the categorization method, each frame in the video buffer is given a specific number for better estimation of the playout outage probability, so it can efficiently handle so many frames from different videos at different bitrates. Meanwhile, at the proposed distribution mechanism, a predetermined threshold value is selected for lower and upper levels of playout outage probability. Then, the control unit at the base station will distribute the radio resources and decide the minimum rate requirement based on clients’ urgency categories. Simulation results showed that the VBF grantees fairness of resources distribution among different clients within the same cellular network while minimizing the interruption duration and controlling the video buffer at an acceptable level. Also, the results showed that the system throughput of the proposed framework outperforms other existing algorithms such as playout buffer and discontinuous reception aware scheduling (PBDAS), maximum carrier‐to‐interface ratio (MAX‐CIR), and proportional fair (PF) due to enhancing the quality of experience for video streaming by increasing the radio resources in fairness manner.

Efficient Multi-View 3D Video Multicast with Depth-Image-Based Rendering in LTE-Advanced Networks with Carrier Aggregation

With the recent emergence of naked-eye 3D mobile devices and various 3D-enabled laptops, service providers now afford the opportunity to provide mobile 3D video streaming in LTE-Advanced networks. Differing from traditional single-view 3D videos, multi-view 3D videos allow users to choose preferred view angles and thus are promising for new applications, such as free-viewpoint television (FTV). Nevertheless, enabling multi-view 3D video services may overwhelm the network resource when transmitting all views of every video. Fortunately, Depth-Image-Based Rendering (DIBR) allows each mobile client to synthesize the desired view from a nearby left view and right view, so that not all views of a video are necessarily transmitted. A new challenge with DIBR, however, is to carefully choose the transmitted views to limit the video distortion and minimize the bandwidth consumption. In this paper, therefore, we first formulate a new optimization problem, called View and MCS Selection (VMS) Problem, to minimize the bandwidth consumption for multi-view 3D video multicast in LTE networks. An algorithm, called View and MCS Aggregation (VMAG) is proposed to find the optimal solution to VMS. For Carrier Aggregation (CA) in LTE-Advanced networks, we formulate a new View, MCS and Carrier Selection (VMCS) Problem and prove that the problem is NP-Hard. We first design a dynamic programming algorithm, called the View Assignment with MCS and Carrier (VAMC) algorithm, to find the optimal solution for small instances. We then propose the View and MCS Aggregation with Carrier (VMAGC) algorithm based on VMAG to effectively find the near-optimal solution to VMCS. The simulation results show that bandwidth consumption can be effectively reduced by over 30 percent in VMS and VMCS.

Optimal Video Streaming in Dense 5G Networks With D2D Communications

Mobile video traffic and mobile devices have now outpaced other data traffic and fixed devices. Global service providers are attempting to propose new mobile infrastructures and solutions for high performance of video streaming services, i.e., high quality of experience (QoE) at high resource efficiency. Although device-to-device (D2D) communications have been an emerging technique that is anticipated to provide a massive number of mobile users with advanced services in 5G networks, the management of resource and co-channel interference between D2D pairs, i.e., helper-requester pairs, and cellular users (CUs) is challenging. In this paper, we design an optimal rate allocation and description distribution for high performance video streaming, particularly, achieving high QoE at high energy efficiency while limiting co-channel interference over D2D communications in 5G networks. To this end, we allocate optimal encoding rates to different layers of a video segment and then packetize the video segment into multiple descriptions with embedded forward error correction before transmission. Simultaneously, the optimal numbers of descriptions are distributed to D2D helpers and base stations in a cooperative scheme for transmitting to the D2D requesters. The optimal results are efficiently in correspondence with intra-popularity of different segments of a video characterized by requesters’ behavior, characteristic of lossy wireless channels, channel state information of D2D requesters, and constraints on remaining energy of D2D helpers and target signal to interference plus noise ratio of CUs. Simulation results demonstrate the benefits of our proposed solution in terms of high performance video streaming.

User Perceived Qos Provisioning for Video Streaming in Wireless OFDMA Systems: Admission Control and Resource Allocation

In this paper, we investigate a joint admission control (AC) and resource allocation (RA) scheme for providing quality of service (QoS) guarantees for video streaming in OFDMA networks. The QoS provisioning is posed as a network utility maximization (NUM) problem. We prove that the QoS requirements are mainly dominated by the average data rate with the aid of diffusion approximation approach, allowing us to decompose the NUM problem into two tractable sub-problems, namely, the AC and the RA sub-problems. We develop an AC algorithm, which enables us to select a maximum user set such that all selected users can meet their QoS requirements related to user perceived video quality during the video playback period. In addition, in order to handle the computational complexity, we further split the RA sub-problem into the constrained Markov decision process associated problems performed at users together with a transmission problem performed at the base station. Correspondingly, we develop a power and sub-carrier allocation algorithm to minimize the average power consumption. The decision of AC is made in a long timescale and depends on the average channel state information (CSI), whereas the decision of RA is made in a short timescale and is subject to both instantaneous CSI and queue state information. Simulation results demonstrate the advantages of the proposed algorithms.

Investigating the Network Characteristics of Two Popular Web-Based Video Streaming Sites

The determinants of the strategies to be employed by video streaming sites are application (mobile devices or web browsers) and container of the video application. They affect video streaming network characteristics, which is often the traffic flow, and its quality. It is against this background that studies on streaming strategies suggested the need to investigate and identify the relationship between buffer time, video stream protocol, packet speed and size, upload time, and waiting period, specifically to aid network administrative support in case of network traffic bottlenecks. In view of this, this study investigates the network characteristics of YouTube and Vimeo, using experimental methodology, and involving WireShark as network analyzer. Google Chrome and Firefox are the web browsers employed, while packet size, protocols, packet interval, TCP window size and accumulation ratio are the metrics. Short ON-OFF, Long ON-OFF, and No ON-OFF cycles are the three streaming strategies identified. It is further shown that both Vimeo and YouTube employ these strategies but the choice depends on the container of the video streamed.

Video Streaming in Content-Centric Mobile Networks: Challenges and Solutions

The massive amounts of mobile traffic generated by the unprecedented demand for high-quality video content are fast approaching the communication capacity of current network infrastructures. Novel solutions are required, including using content- centric mobile networks (CCMNs), which make use of storage, computation, and bandwidth resources of the entire network to support traffic offloading and large-scale content sharing in wireless mobile networks. The unicast-based interest routing in CCMNs is known for fast content delivery with low resource consumption, but video information management and mobility of mobile nodes significantly influence the performance of video delivery. This article first reviews recent studies and discusses the research challenges for content distribution and delivery in CCMNs. The article then presents an innovative video streaming solution in CCMNs (VSCC), which proactively leverages a content-centric multi-region video content management method and a mobility-adaptive content-centric video delivery mechanism to improve the performance of video delivery. Simulation results demonstrate that VSCC achieves high video sharing efficiency and increased QoS levels in comparison with two state of the art approaches.

Modeling Buffer Starvations of Video Streaming in Cellular Networks with Large-Scale Measurement of User Behavior

Unraveling quality of experience (QoE) of video streaming is very challenging in bandwidth shared wireless networks. It is unclear how QoE metrics such as starvation probability and buffering time interact with dynamics of streaming traffic load. In this paper, we collect view records from one of the largest streaming providers in China over two weeks and perform an in-depth measurement study on flow arrival and viewing time that shed light on the real traffic pattern. Our most important observation is that the viewing time of streaming users fits a hyper-exponential distribution quite well. This implies that all the views can be categorized into two classes, short and long views with separated time scales. We then map the measured traffic pattern to bandwidth shared cellular networks and propose an analytical framework to compute the closed-form starvation probability on the basis of ordinary differential equations (ODEs). Our framework can be naturally extended to investigate practical issues including the progressive downloading and the finite video duration. Extensive trace-driven simulations validate the accuracy of our models. Our study reveals that the starvation metrics of the short and long views possess different sensitivities to the scheduling priority at base station (BS). Hence, a better QoE tradeoff between the short and long views has a potential to be leveraged by offering them different scheduling weights. The flow differentiation involves tremendous technical and non-technical challenges because video content is owned by content providers but not the network operators and the viewing time of each session is unknown beforehand. To overcome these difficulties, we propose an online Bayesian approach to infer the viewing time of each incoming flow with the “least” information from content providers.

Optimizing power consumption of mobile devices for video streaming over 4G LTE networks

While the video streaming technique and 4G LTE networks are developing rapidly, the advancement of the battery technology, however, is relatively slow. Existing works have paid much attention to understanding the power consumption in general 4G LTE networks, while few attention has been paid to reducing the power consumption for online video streaming in 4G LTE networks. Our previous work based on the real experimental platform showed that, the saving room in the network part is large, the transmission pattern and the number of RRC tails could be promising for optimizing the power consumption. In this paper, we attempt to develop efficient optimization strategies to save the energy cost of mobile devices for online video streaming over 4G LTE networks. This problem is clearly important and also interesting, while it is challenging, due to various reasons such as the uncertainty of users’ behavior modes. To alleviate the challenges, we suggest a self-adaptive method that can allow us to adjust various parameters dynamically and efficiently, so as to achieve a relatively small energy consumption. We give the rigorous theoretical analysis for the proposed method, and conduct extensive experiments to validate its effectiveness. The experimental results show that the proposed method consistently outperforms the classical method as well as other competitors adapted from existing methods.

Power consumption analysis of video streaming in 4G LTE networks

Video streaming, one of the most popular technologies for online video playback, has already been applied to 4G LTE networks. Previous work has been devoted to understanding the power consumption in general 4G LTE networks, while it is still unclear how the online video streaming makes impact on the power performance of mobile devices. Inspired by this, this paper investigates the relationship between the mobile device's power performance characteristics and the behaviours of video streaming in 4G LTE networks. There are many natural issues/questions that are clearly interesting and important, while it is non-trivial to answer these issues/questions exactly (e.g., where is the energy saving room? how much is it?). To address a series of issues like the above, we formulate our energy models together with an algorithm that can assist our analysis. Particularly, we design a systematic platform, and conduct a comprehensive and also deep analysis on the power consumption of video streaming in 4G LTE networks. Our experiments reveal us a series of valuable findings--the saving room in the network part is large (from 41.86 to 69.62%), the number of RRC tails and the transmission pattern could be promising for optimizing the power consumption, for example.

Optimizations and Economics of Crowdsourced Mobile Streaming

Mobile video traffic accounts for more than half of the global mobile data traffic nowadays, and the ratio is expected to further increase in the near future. However, providing high quality of experience for video streaming in mobile networks is challenging due to the heterogeneous and varying wireless channel conditions. To meet the increasing demand of high-quality mobile video streaming services, researchers have proposed several cooperative video streaming models that enable mobile users to download video contents cooperatively. The key idea is to pool network edge resources so as to either alleviate the load on the video servers and the cellular network, or alleviate the impact of channel variations and improve resource utilization. In this article, we review four types of cooperative video streaming models that pool various network resources effectively in different application scenarios. Then we focus on the crowdsourced mobile streaming model, which aims to pool users' download capacities in order to alleviate the impact of channel variations and achieve efficient utilization of network resources. We introduce the corresponding optimization issue of efficient resource allocation and the economic issue of user cooperation. We also outline future challenges and open issues in cooperative video streaming models.

A Cross-Layer Bandwidth Allocation Scheme for HTTP-Based Video Streaming in LTE Cellular Networks

This letter investigates the benefits of flexible resource allocation when performing HTTP-based adaptive streaming (HAS) across cellular systems such as long-term evolution (LTE). To guarantee video fluidity in the presence of fluctuations of the instantaneous video source rate and channel capacity, we consider an HAS-based proxy video manager and resource controller located at the cellular base station. Based on the channel quality observed by mobile clients, the manager allocates the wireless bandwidth to mobile clients for transmitting the video streams. We propose a cross-layer bandwidth allocation scheme that takes into account the channel quality as well as the video quality requirements and encoding rate fluctuations of the HAS video stream and minimizes the transmission delays experienced by users. This cross-layer bandwidth allocation achieves the optimum in terms of HAS streams delays and it outperforms different bandwidth allocations procedures and state-of-the-art LTE schedulers.

Robust distributed video coding for wireless multimedia sensor networks

Coding complexity and error-resilience are the two key factors for video streaming in Wireless Multimedia Sensor Networks (WMSNs). Towards this objective, this paper proposes a Robust Distributed Video Coding (RDVC) framework to optimize the quality of video transmission for WMSNs. A new error-resilient Key frame coding scheme is proposed based on the protection of Wyner-Ziv Coding (WZC), in which additional Wyner-Ziv bits play a significant role for better error resilience and better Rate/Distortion (RD) performance. In addition, following the theoretical background of Distributed Joint Source Channel Coding (DJSCC), a novel distributed source channel codec based on Group Puncture Rate Adaptive IRA code (GPRA-IRA) is proposed, upon which the error-resilient Wyner-Ziv codec is designed. These experimental results show that the proposed RDVC scheme outperforms the relevant coding in terms of RD performance, notably by up to 1-3 dB, while achieving a lower encoding complexity.

A new mechanism to improve video streaming in P2P networks using helper nodes

Peer-to-peer video on demand (P2P-VoD) is a solution to provide video service to a large number of users on the Internet. Due to the upload bandwidth bottleneck of VoD servers, the streaming capacity in the P2P-VoD system is limited. This paper proposes a new mechanism to improve the video streaming in P2P networks by using the idle peers upload bandwidth and storage, which are called helpers. The proposed method uses an XOR mechanism in the VoD server to encode chunks and reduce bandwidth use. The helpers receive encoded chunks from the VoD server and save them in their storage spaces and cooperate in sharing these chunks with the other peers. Simulation results using OMNET++ are provided to show the efficiency of the proposed mechanism in terms of the required upload capacity requirement of the server and deploying the storage and bandwidth of helpers. The results of simulation show that the upload bandwidth usage of the server has been reduced. On the other hand, the proposed method increases the memory usage of helpers.

Secure Channel Allocation Approach for Video Streaming in Cognitive Radio Networks

In this paper, we propose a technique to ensure security in cooperative spectrum sensing for video streaming application in cognitive radio network. Sensing based clustering algorithm puts together the cognitive radios into clusters to mitigate cooperative overhead and to reduce energy consumption. In order to avert primary user emulation attacks, we introduce authentication signature ID algorithm which provides authority for the licensed user to use the spectrum. To diminish spectrum sensing data falsification attacks, we exploit hamming algorithm to expel forgery node from spectrum sensing decision. Furthermore, the primary radio network channel allocation algorithm is employed to precisely deliver the video with high quality and lower delay. For video transmission in a cognitive radio network, we compress the video frames using block truncation coding-pattern fitting in order to reduce bandwidth requirement. This compression technique offers good video transmission with a high compression ratio in a cognitive radio network, due to trade-off among quality, bit rate and decoding time. Simulation results reveal that there is a significant improvement in the detection rate of idle channel and reduction of delay for high-quality video transmission.

An Analysis of Quality of Service (QoS) In Live Video Streaming Using Evolved HSPA Network Media

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>Evolved High Speed Packet Access (HSPA+) is a mobile telecommunication system technology and the evolution of HSPA technology. This technology has a packet data based service with downlink speeds up to 21.1 Mbps and uplink speed up to 11.5 Mbps on the bandwidth 5MHz. This technology is expected to fulfill and support the needs for information that involves all aspects of multimedia such as video and audio, especially live video streaming. By utilizing this technology it will facilitate communicating the information, for example to monitoring the situation of the house, the news coverage at some certain area, and other events in real time. This thesis aims to identify and test the Quality of Service (QoS) performance on the network that is used for live video streaming with the parameters of throughput, delay, jitter and packet loss. The software used for monitoring the data traffic of the live video streaming network is wireshark network analyzer. From the test results it is obtained that the average throughput of provider B is 5,295 Kbps bigger than the provider A, the average delay of provider B is 0.618 ms smaller than the provider A, the average jitter of provider B is 0.420 ms smaller than the provider A and the average packet loss of provider B is 0.451% smaller than the provider A.</span></p></div></div></div>

Proactive Caching for Mobile Video Streaming in Millimeter Wave 5G Networks

Mobile video streaming is fundamental to advanced applications in the fifth generation (5G) networks. Millimeter wave (mmWave) communication represents a leading 5G technology, which provides rich bandwidth and, therefore, great potentials for high-quality mobile video streaming. However, mobile video streaming in mmWave 5G networks faces fundamental challenges due to mmWave antenna directivity and high user mobility. As such, users typically have short connection durations and frequent handoffs, making video streaming suffer from long handoff delays and connection latency. In this paper, we tackle the issues by developing a caching-based mmWave framework, which precaches video contents at the base station for handoff users and thus significantly reduces the connection and retrieval delays. As a result, high-mobility users with frequent handoffs can enjoy continuous high-quality video streaming. Specifically, we model the proposed system as a cache management problem and attain optimal video streaming quality by using Markov decision process to dynamically allocate proper cache memory space of each base station to mobile users. A cell-by-cell decomposition method is proposed to solve the dynamic programming problem with significantly reduced computational complexity. Using extensive simulations, we demonstrate that the proposed solution can effectively maintain high-quality mobile video streaming for high-mobility 5G users moving among mmWave small cells with directional antenna.

Resource Allocation for Video Streaming in Heterogeneous Cognitive Vehicular Networks

In future fifth-generation (5G) communication systems, driven by the evolution of today's most demanding applications, video streaming over vehicle networks will play an increasingly significant role in our daily lives. In this paper, based on a heterogeneous architecture consisting of both cellular base stations (BSs) of wide coverage and cognitive-radio-enabled roadside infrastructures, a semi-Markov decision process (SMDP)-based resource-allocation scheme is proposed to facilitate video streaming application in terms of peak signal-to-noise ratio (PSNR) and smooth playback. In addition to improving video quality of vehicle users, quality of service (QoS) provisioning for background users that can originally exist in the cellular network is also considered in the proposed scheme. Specifically, based on the states of the background users, vehicle users, and the availability of the cognitive bands, the optimal resource allocation, aiming at maximizing the video streaming quality while guaranteeing the call-level performance of the background users, is achieved by addressing two interrelated joint call admission control (CAC) and channel allocation problems for cellular and roadside infrastructure networks, respectively. Simulation results show that the proposed scheme can well protect background users when coexisting with bandwidth-hungry vehicle video users and is effective in not only improving video streaming quality for vehicle users but boosting bandwidth utilization of the whole network as well.

ICN based vehicle-to-cloud delivery for multimedia streaming in urban vehicular networks

Mobile multimedia streaming is an open topic in vehicular environment. Due to the high intermittent links, it has become a critical challenge to deliver high quality video streaming in vehicular networks. In this paper, we reform the Information Centric Networking (ICN) concept for multimedia delivery in urban vehicular networks. By leveraging the ICN perspective, we highlight that vehicular peers can obtain multimedia chunks via the vehicle-to-cloud (V2C) approach to improve the delivery quality. Based on this, we propose a lightweight multipath selection strategy to guide the network system to adaptively adjust the forwarding means. Extensive simulations show that the proposed solution can optimize the utilization of network paths, lighten network loads as well as avoid wasting resources.

Supporting Quality of Experience based on Service Level Agreement for Heterogeneous Networks

In this paper we propose a novel QoE control scheme based on Service Level Agreement (SLA) which enables to guarantee the user perceived service quality of various high-quality mobile multimedia services such as mobile video streaming and real-time network games to a certain level. For this purpose, a SLA control structure is adopted to provision QoE, and a novel resource management scheme gets coupled with this control structure. Simulation results show that our proposed scheme can decrease the average delay and increase the total throughput by adjusting SLA.

Quality of Experience Driven Multi-User Video Streaming in Cellular Cognitive Radio Networks With Single Channel Access

We investigate the problem of streaming multi-user videos over the downlink of a cognitive radio network (CRN), where each cognitive user (CU) can access one channel at a time. We first consider the case where each CU can sense one channel at a time slot at most. To make the problem tractable, we tackle the optimal spectrum sensing and access problems separately and develop matching-based optimal algorithms to the subproblems, which yield an overall suboptimal solution. We then consider the case where each CU can sense multiple channels. We show that under the assumption that all the spectrum sensors work on the same operating point, a two-step approach can derive the optimal spectrum sensing and access policies that maximize the quality of experience (QoE) of the streaming videos. The superior performance of the proposed approaches is validated with simulations and comparisons with benchmark schemes, where a performance gain from 25% to 30% is demonstrated.