A utility-based priority scheduling scheme for multimedia delivery over LTE networks

With the mobile networks migrating towards LTE-Advanced and all-IP networks, people expect to connect to the Internet anytime, anywhere and from any IP-connected device. Moreover, nowadays people tend to spend much of their time consuming multimedia content from various devices with heterogeneous characteristics (e.g., TV screen, laptop, tablet, smartphone, etc.). In order to support uninterrupted, continuous, and smooth video streaming with reduced delay, jitter, and packet loss to their customers, network operators must be able to differentiate between their offerings according to device characteristics, including screen resolution. This paper proposes a novel Utility-based Priority Scheduling (UPS) algorithm which considers device differentiation when supporting high quality delivery of multimedia services over LTE networks. The priority decision is based on device classification, mobile device energy consumption and multimedia stream tolerance to packet loss ratio. Simulation results demonstrate the benefits of the proposed priority-based scheduling algorithm in comparison with two classic approaches

Device-oriented energy-aware utility-based priority scheduler for video streaming over LTE system

Nowadays people tend to spend most of their time in front of a screen, and expect to be able to connect to the Internet anytime and anywhere and from any type of mobile device. Therefore, fast surfing speed on Internet, high resolution display screen, advanced multi-core processor and lasting battery support are becoming the significant standards in the nowadays mobile devices. In this context the network operators must be able to differentiate between their multiscreen offerings in order to ensure uninterrupted, continuous, and smooth video streaming with minimal delay, jitter, and packet loss. This paper proposes a novel Device-Oriented Energy-Aware Utility-based Priority scheduling (DE-UPS) algorithm which makes use of device differentiation in order to ensure seamless multimedia services over LTE networks. The priority decision is based on the device classification, energy consumption of the mobile device and the multimedia stream tolerance to packet loss ratio

Random Linear Network Coding for 5G Mobile Video Delivery

An exponential increase in mobile video delivery will continue with the demand for higher resolution, multi-view and large-scale multicast video services. Novel fifth generation (5G) 3GPP New Radio (NR) standard will bring a number of new opportunities for optimizing video delivery across both 5G core and radio access networks. One of the promising approaches for video quality adaptation, throughput enhancement and erasure protection is the use of packet-level random linear network coding (RLNC). In this review paper, we discuss the integration of RLNC into the 5G NR standard, building upon the ideas and opportunities identified in 4G LTE. We explicitly identify and discuss in detail novel 5G NR features that provide support for RLNC-based video delivery in 5G, thus pointing out to the promising avenues for future research.Comment: Invited paper for Special Issue "Network and Rateless Coding for Video Streaming" - MDPI Informatio

An intelligent fuzzy logic-based content and channel aware downlink scheduler for scalable video over OFDMA wireless systems

The recent advancements of wireless technology and applications make downlink scheduling and resource allocations an important research topic. In this paper, we consider the problem of downlink scheduling for multi-user scalable video streaming over OFDMA channels. The video streams are precoded using a scalable video coding (SVC) scheme. We propose a fuzzy logic-based scheduling algorithm, which prioritises the transmission to different users by considering video content, and channel conditions. Furthermore, a novel analytical model and a new performance metric have been developed for the performance analysis of the proposed scheduling algorithm. The obtained results show that the proposed algorithm outperforms the content-blind/channel aware scheduling algorithms with a gain of as much as 19% in terms of the number of supported users. The proposed algorithm allows for a fairer allocation of resources among users across the entire sector coverage, allowing for the enhancement of video quality at edges of the cell while minimising the degradation of users closer to the base station

An innovative machine learning-based scheduling solution for improving live UHD video streaming quality in highly dynamic network environments

The latest advances in terms of network technologies open up new opportunities for high-end applications, including using the next generation video streaming technologies. As mobile devices become more affordable and powerful, an increasing range of rich media applications could offer a highly realistic and immersive experience to mobile users. However, this comes at the cost of very stringent Quality of Service (QoS) requirements, putting significant pressure on the underlying networks. In order to accommodate these new rich media applications and overcome their associated challenges, this paper proposes an innovative Machine Learning-based scheduling solution which supports increased quality for live omnidirectional (360◦) video streaming. The proposed solution is deployed in a highly dy-namic Unmanned Aerial Vehicle (UAV)-based environment to support immersive live omnidirectional video streaming to mobile users. The effectiveness of the proposed method is demonstrated through simulations and compared against three state-of-the-art scheduling solutions, such as: Static Prioritization (SP), Required Activity Detection Scheduler (RADS) and Frame Level Scheduler (FLS). The results show that the proposed solution outperforms the other schemes involved in terms of PSNR, throughput and packet loss rate