Improving Video Streaming Quality Research Articles

QoE awareness in progressive caching and DASH-based D2D video streaming in cellular networks

In this paper, we present an architecture to improve video streaming quality of experience (QoE) in cellular networks with high user equipment (UE) density. In the proposed architecture, video segments are progressively cached, as requested, in selected UEs called storage members (SMs). Video segments are strategically cached to be available to requesting users in the cell. Furthermore, the base-station controls the device-to-device communication between the UEs to provide collaborative peer-to-peer transmission of video segments. Dynamic adaptive streaming over HTTP is also employed to adapt the quality of video segments to network conditions. We study the improvements achieved by the proposed architecture in terms of many video streaming QoE metrics. Thereafter, we improve the operation of the proposed architecture by introducing QoE awareness to both caching and distribution of video segments. We employ QoE awareness in three aspects of the proposed architecture; cellular resource allocation, caching of video segments, and SM-assignment optimization. We analyze the improvements achieved by the prospered QoE-awareness techniques in terms of video streaming QoE metrics.

Improving Video Streaming Quality in 5G Enabled Vehicular Networks

The 5G mobile communication technologies are the most prominent candidates for vehicular networks to support various types of applications. In this article, we describe several 5G technologies and how these technologies support various types of vehicular applications. Since high-quality video streaming over vehicular networks is one of the most popular on-road entertainment applications for passengers, this article discusses the challenges of video streaming over 5G enabled vehicular networks. To achieve better video streaming quality, we introduce a double-buffer system to mitigate the delay effect due to a vehicle's frequent handoffs among 5G small cells and the intermittent connection effect resulting from millimeter-wave propagation features. We also discuss potential future work on joint optimization of buffer space and bandwidth allocation for video streaming, and maintaining connectivity for real-time applications in 5G enabled vehicular networks.

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.

Location-based social video sharing over next generation cellular networks

The popularity of video sharing over social networks is straining service providers to maintain high quality of service. With the introduction of live video streaming over social networks, video sharing is truly becoming social and real time. However, existing video distribution architecture, split across content providers and mobile network operators, cannot leverage crossprovider information. We argue that MNOs can explore the knowledge of users' location to route video traffic intelligently. This can enable better quality of experience for live social video sharing. We show different scenarios in existing cellular networks, where user experience can suffer due to current video distribution mechanism. We propose that use of the location information of the receiver with respect to the sender can be exploited by next generation cellular networks for improving video streaming quality. Our simulation experiments validate that on all QoE metrics there is opportunity for significant improvement in live video streaming by using relative locations of the receivers and senders.

A Real-Time Adaptive Algorithm for Video Streaming over Multiple Wireless Access Networks

Video streaming is gaining popularity among mobile users. The latest mobile devices, such as smart phones and tablets, are equipped with multiple wireless network interfaces. How to efficiently and cost-effectively utilize multiple links to improve video streaming quality needs investigation. In order to maintain high video streaming quality while reducing the wireless service cost, in this paper, the optimal video streaming process with multiple links is formulated as a Markov Decision Process (MDP). The reward function is designed to consider the quality of service (QoS) requirements for video traffic, such as the startup latency, playback fluency, average playback quality, playback smoothness and wireless service cost. To solve the MDP in real time, we propose an adaptive, best-action search algorithm to obtain a sub-optimal solution. To evaluate the performance of the proposed adaptation algorithm, we implemented a testbed using the Android mobile phone and the Scalable Video Coding (SVC) codec. Experiment results demonstrate the feasibility and effectiveness of the proposed adaptation algorithm for mobile video streaming applications, which outperforms the existing state-of-the-art adaptation algorithms.