Impact of the LTE scheduler on achieving good QoE for DASH video streaming

Dynamic adaptive video over HTTP (DASH) is fast becoming the protocol of choice for content providers for their online video streaming delivery. Concurrently, dependence on cellular Long Term Evolution (LTE) networks is growing to serve user demands for bandwidth-hungry applications, especially video. Each LTE base station's (eNodeB) scheduler assigns wireless resources to individual clients. Several alternative schedulers have been proposed, especially to meet the user's desired quality of experience (QoE) with video. In this paper, we investigate the impact of the scheduler on DASH performance, motivated by the fact that video performance and the underlying traffic models are different from other HTTP/TCP applications. We use our laboratory testbed employing real video content and streaming clients, over a simulated ns-3 LTE network. We quantify the impact of the scheduler and show that it has a significant impact on key video streaming performance metrics such as stalls and QoE, for different client adaptation algorithms. Additionally, we show the impact of user mobility within a cell, which has the side-effect of improving performance by mitigating long-term fading effects. Our detailed assessment of four LTE schedulers in ns-3 shows that the proportional fair scheduler achieves the best overall user experience, although somewhat disadvantaging static cell-edge users

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

MSPlayer: Multi-Source and multi-Path LeverAged YoutubER

Online video streaming through mobile devices has become extremely popular nowadays. YouTube, for example, reported that the percentage of its traffic streaming to mobile devices has soared from 6% to more than 40% over the past two years. Moreover, people are constantly seeking to stream high quality video for better experience while often suffering from limited bandwidth. Thanks to the rapid deployment of content delivery networks (CDNs), popular videos are now replicated at different sites, and users can stream videos from close-by locations with low latencies. As mobile devices nowadays are equipped with multiple wireless interfaces (e.g., WiFi and 3G/4G), aggregating bandwidth for high definition video streaming has become possible. We propose a client-based video streaming solution, MSPlayer, that takes advantage of multiple video sources as well as multiple network paths through different interfaces. MSPlayer reduces start-up latency and provides high quality video streaming and robust data transport in mobile scenarios. We experimentally demonstrate our solution on a testbed and through the YouTube video service.Comment: accepted to ACM CoNEXT'1

Cross-layer Optimization for Video Delivery over Wireless Networks

As video streaming is becoming the most popular application of Internet mo- bile, the design and the optimization of video communications over wireless networks is attracting increasingly attention from both academia and indus- try. The main challenges are to enhance the quality of service support, and to dynamically adapt the transmitted video streams to the network condition. The cross-layer methods, i.e., the exchange of information among different layers of the system, is one of the key concepts to be exploited to achieve this goals. In this thesis we propose novel cross-layer optimization frameworks for scalable video coding (SVC) delivery and for HTTP adaptive streaming (HAS) application over the downlink and the uplink of Long Term Evolution (LTE) wireless networks. They jointly address optimized content-aware rate adaptation and radio resource allocation (RRA) with the aim of maximiz- ing the sum of the achievable rates while minimizing the quality difference among multiple videos. For multi-user SVC delivery over downlink wireless systems, where IP/TV is the most representative application, we decompose the optimization problem and we propose the novel iterative local approxi- mation algorithm to derive the optimal solution, by also presenting optimal algorithms to solve the resulting two sub-problems. For multiple SVC de- livery over uplink wireless systems, where healt-care services are the most attractive and challenging application, we propose joint video adaptation and aggregation directly performed at the application layer of the transmit- ting equipment, which exploits the guaranteed bit-rate (GBR) provided by the low-complexity sub-optimal RRA solutions proposed. Finally, we pro- pose a quality-fair adaptive streaming solution to deliver fair video quality to HAS clients in a LTE cell by adaptively selecting the prescribed (GBR) of each user according to the video content in addition to the channel condi- tion. Extensive numerical evaluations show the significant enhancements of the proposed strategies with respect to other state-of-the-art frameworks