Raptor Code-Aware Link Adaptation for Spectrally Efficient Unicast Video Streaming over Mobile Broadband Networks

This paper proposes novel Raptor-aware link adaptation (LA) when application layer Forward Error Correction (AL-FEC) with Raptor codes is used for live, high quality, video unicast over mobile broadband networks. The use of Raptor code AL-FEC is taken into account for the adaptation of the modulation and coding scheme (MCS) used in the physical layer. A cross-layer optimization approach is used to select the Raptor code parameters and the MCS mode jointly, in order to maximize transmission efficiency.

The proposed methodology takes into consideration the channel resources required to accommodate the Raptor overheads. Simulation results show that packet loss is eliminated and the amount of radio resource required is reduced significantly. Automatic repeat request (ARQ) based unicast systems require up to 115.6 percent more channel resources, by comparison to the proposed Raptor-aware LA system without retransmissions. Furthermore, the Raptor-aware LA system can enhance the link budget by up to 4 dB, increasing coverage in LoS locations, and can improve total goodput by 46.7 percent compared to an ARQ-based system.

Availability and sensitivity analysis in video streaming services hosted on private clouds

Cloud computing have one technology development that favor various computing services and storage over the Internet. The multimedia services are examples of these new services will now use this new technology. About the multimedia services, we highlight the streaming video service, where the user can access your videos from these cloud environments with the same quality practiced in traditional environments.

In this paper, we used the models to find the RBD availability of streaming videoservices in a test environment assembled in the laboratory to evaluate the system. Sensitivity analysis was used to identify the critical points of the system with respect to availability so that you can act on them and thus improve the system availability.

Beacon-less video streaming management for VANETs based on QoE and link-quality

Real-time video dissemination over Vehicular Ad hoc Networks (VANETs) is fundamental for many services, e.g., emergency video delivery, road-side video surveillance, and advertisement broadcasting. These applications deal with several challenges due to strict video quality level requirements and highly dynamic topologies. To handle these challenges, geographic receiver-based beacon-less approaches have been proposed as a suitable solution for forwarding video flows in VANETs. In general, the routing decisions are performed only based on network, link, and/or node characteristics, such as link quality and vehicle’s location.

However, in real situations, due to different requirements and hierarchical structures of multimedia applications, these existent routing decisions are not satisfactory to select the best relay nodes and build up reliable backbones to delivery video content with reduced delay and high Quality of Experience (QoE). This paper introduces the QOe-Driven and LInk-qualiTy rEceiver-based (QOALITE) protocol to allow live video dissemination with QoE assurance in Vehicle-to-Vehicle (V2V) scenarios. QOALITE considers video and QoE-awareness, coupled with location and link quality attributes for relay selection. Simulation results show the benefits of QOALITE when compared to existing work, while achieving multimedia transmission with QoE support and robustness in highway scenarios.

Scalable Bit Allocation Between Texture and Depth Views for 3-D Video Streaming Over Heterogeneous Networks

In the multiview video plus depth (MVD) coding format, both texture and depth views are jointly compressed to represent the 3-D video content. The MVD format enables synthesis of virtual views through depth-image-based rendering; hence, distortion in the texture and depth views affects the quality of the synthesized virtual views. Bit allocation between texture and depth views has been studied with some promising results. However, to the best of our knowledge, most of the existing bit-allocation methods attempt to allocate a fixed amount of total bit rate between texture and depth views; that is, to select appropriate pair of quantization parameters for texture and depth views to maximize the synthesized view quality subject to a fixed total bit rate. In this paper we propose a scalable bit-allocation scheme, where a single ordering of texture and depth packets is derived and used to obtain optimal bit allocation between texture and depth views for any total target rates.

In the proposed scheme, both texture and depth views are encoded using the quality scalable coding method; that is, medium grain scalable (MGS) coding of the Scalable Video Coding (SVC) extension of the AdvancedVideo Coding (H.264/AVC) standard. For varying target total bit rates, optimal bit truncation points for both texture and depth views can be obtained using the proposed scheme. Moreover, we propose to order the enhancement layer packets of the H.264/SVC MGS encoded depth view according to their contribution to the reduction of the synthesized view distortion. On one hand, this improves the depth view packet ordering when considered the rate-distortion performance of synthesized views, which is demonstrated by the experimental results. On the other hand, the information obtained in this step is used to facilitate optimal bit allocation between texture and depth views. Experimental results demonstrate the effectiveness of the proposed scalable bit-allocation scheme for texture and depth view- .

An FPTAS for managing playout stalls for multiple video streams in cellular networks

With the proliferation of wireless cellular technology (e.g., 5G, 4G LTE), managing quality of experience (QoE) for wireless clients streaming different videos over these networks is becoming increasingly important. In this paper, we consider the problem of managing playout stalls experienced by multiple clients streaming different videos from a cellular base station. We build on our prior work [1], where we formulate an epoch based lead aware multiple video transmission (LMVT) problem to minimize the total number of playout stalls experienced by mobile clients.

In [1], [2], we show that the LMVT problem is NP-hard and propose a pseudo polynomial dynamic programming solution and a simple greedy heuristic to solve the problem. In this paper, we first show that even a simpler and less restrictive version of the LMVT problem remains hard. We then design a fully polynomial time approximation scheme (FPTAS) for the LVMT problem by demonstrating that it is equivalent to the Santa Claus Problem [3], [4]. The FPTAS provides a bounded performance guarantee, differing by a factor of 1/1+ϵB from the optimal solution (where B is the number of time slots in an epoch and ε is a small constant).

Attentive Monitoring of Multiple Video Streams Driven by a Bayesian Foraging Strategy

In this paper, we shall consider the problem of deploying attention to the subsets of the video streamsfor collating the most relevant data and information of interest related to a given task. We formalize this monitoring problem as a foraging problem. We propose a probabilistic framework to model observer’s attentive behavior as the behavior of a forager. The forager, moment to moment, focuses its attention on the most informative stream/camera, detects interesting objects or activities, or switches to a more profitable stream.

The approach proposed here is suitable to be exploited for multi-stream videosummarization. Meanwhile, it can serve as a preliminary step for more sophisticated video surveillance, e.g., activity and behavior analysis. Experimental results achieved on the UCR Videoweb Activities Data Set, a publicly available data set, are presented to illustrate the utility of the proposed technique.

Using Viewing Statistics to Control Energy and Traffic Overhead in Mobile Video Streaming

Video streaming can drain a smartphone battery quickly. A large part of the energy consumed goes to wireless communication. In this article, we first study the energy efficiency of different video content delivery strategies used by service providers and identify a number of sources of energy inefficiency. Specifically, we find a fundamental tradeoff in energy waste between prefetching small and large chunks of video content: small chunks are bad because each download causes a fixed tail energy to be spent regardless of the amount of content downloaded, whereas large chunks increase the risk of downloading data that user will never view because of abandoning the video.

Hence, the key to optimal strategy lies in the ability to predict when the user might abandon viewing prematurely. We then propose an algorithm called eSchedule that uses viewing statistics to predict viewer behavior and computes an energy optimal download strategy for a given mobile client. The algorithm also includes a mechanism for explicit control of traffic overhead, i.e., unnecessary download of content that the user will never watch. Our evaluation results suggest that the algorithm can cut the energy waste down to less than half compared to other strategies. We also present and experiment with an Android prototype that integrates eSchedule into a YouTube downloader.

Energy-Efficient Adaptive Transmission of Scalable Video Streaming in Cognitive Radio Communications

Cognitive radio (CR) is a promising technology to alleviate spectrum shortage and satisfy the huge demand of bandwidth for multimedia streaming in future mobile computing systems. The inherent features of CR pose tough challenges in provisioning quality of service (QoS) for acceptable user experience and minimizing energy consumption for multimedia transmissions. In this paper, scalablevideo coding and transmission rate adaptation are jointly considered in an energy-efficient scheme for transmissions of streaming media over CR with QoS guarantee.

An event-driven discrete-time Markov control process model is introduced to formulate the QoS-guaranteed energy-efficient transmission problem as a constrained stochastic optimization problem. Based on estimations of potentials and the difference between performance measurement and QoS requirement, an online policy iteration algorithm is proposed to optimize energy consumption under QoS constraints directly. By exploiting the system dynamics, this algorithm does not depend on any prior knowledge of channel availability or fading statistics, and it can converge to a near optimum with a low computational burden. Simulation results demonstrate the effectiveness of the proposed method.

Optimal Beamforming for Video Streaming in Multiantenna Interference Networks via Diffusion Limit

In this paper, we consider queue-aware beamforming control for video streaming applications in multiantenna interference network. Using heavy traffic approach, we derive the diffusion limit for the discrete time queuing system and obtain an ergodic excess beamforming control problem for the diffusion limit system. The ergodic control problem is to minimize the average power costs of the access points subject to the constraints on the playback interruption costs and buffer overflow costs of the mobile users. To deal with the queue coupling challenge, we utilize the weak interference coupling property in the network.

Using the theories of calculus and perturbation techniques, we derive a closed-form approximate priority function of the optimality equation and the associated error bound. Based on this approximation, we propose a low complexity queue-aware beamforming control algorithm, which is asymptotically optimal for sufficiently small cross-channel path gain. Finally, the proposed scheme is compared with various baselines through simulations and it is shown that significant performance gain can be achieved.

4G LTE architectural and functional models of Video Streaming and VoLTE services

User experience about the provisioning of a service over LTE mobile networks has become a crucial aspect for Mobile Network Operators. Monitoring network performances may not be sufficient, because they have to be correlated to the specific service experienced by the user. In order to do this, it is important to model network architecture in relation to the service.

For this reason, in this paper, several LTE functional models have been proposed, for real time services like VoLTE and Video Streaming with MNO’s CDN and OTT, in which the standardized LTE architecture is strictly modeled on the service offered to the user. The main contribution of this research relies on the possibility for the MNO to adopt the 4G architectural LTE Video Streaming and VoLTE network, based on the respectives functional model, to conduct the accurate QoE assessment.