Improving Mobile Video Streaming with Mobility Prediction and Prefetching in Integrated Cellular-WiFi Networks

We present and evaluate a procedure that utilizes mobility and throughput prediction to prefetch video streaming data in integrated cellular and WiFi networks. The effective integration of such heterogeneous wireless technologies will be significant for supporting high performance and energy efficient video streaming in ubiquitous networking environments. Our evaluation is based on trace-driven simulation considering empirical measurements and shows how various system parameters influence the performance, in terms of the number of paused video frames and the energy consumption; these parameters include the number of video streams, the mobile, WiFi, and ADSL backhaul throughput, and the number of WiFi hotspots. Also, we assess the procedure's robustness to time and throughput variability. Finally, we present our initial prototype that implements the proposed approach.Comment: 7 pages, 15 figure

Seamless Dynamic Adaptive Streaming in LTE/Wi-Fi Integrated Network under Smartphone Resource Constraints

Exploiting both LTE and Wi-Fi links simultaneously enhances the performance of video streaming services in a smartphone. However, it is challenging to achieve seamless and high quality video while saving battery energy and LTE data usage to prolong the usage time of a smartphone. In this paper, we propose REQUEST, a video chunk request policy for Dynamic Adaptive Streaming over HTTP (DASH) in a smartphone, which can utilize both LTE and Wi-Fi. REQUEST enables seamless DASH video streaming with near optimal video quality under given budgets of battery energy and LTE data usage. Through extensive simulation and measurement in a real environment, we demonstrate that REQUEST significantly outperforms other existing schemes in terms of average video bitrate, rebuffering, and resource waste.Peer reviewe

QoE management of multimedia streaming services in future networks : a tutorial and survey

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Dissecting the performance of VR video streaming through the VR-EXP experimentation platform

To cope with the massive bandwidth demands of Virtual Reality (VR) video streaming, both the scientific community and the industry have been proposing optimization techniques such as viewport-aware streaming and tile-based adaptive bitrate heuristics. As most of the VR video traffic is expected to be delivered through mobile networks, a major problem arises: both the network performance and VR video optimization techniques have the potential to influence the video playout performance and the Quality of Experience (QoE). However, the interplay between them is neither trivial nor has it been properly investigated. To bridge this gap, in this article, we introduce VR-EXP, an open-source platform for carrying out VR video streaming performance evaluation. Furthermore, we consolidate a set of relevant VR video streaming techniques and evaluate them under variable network conditions, contributing to an in-depth understanding of what to expect when different combinations are employed. To the best of our knowledge, this is the first work to propose a systematic approach, accompanied by a software toolkit, which allows one to compare different optimization techniques under the same circumstances. Extensive evaluations carried out using realistic datasets demonstrate that VR-EXP is instrumental in providing valuable insights regarding the interplay between network performance and VR video streaming optimization techniques

Análisis del servicio de video streaming basado en el algoritmo FDASH sobre LTE

Video Streaming is the service that generates the highest percentage of data trafc inlong-term evolution (LTE) mobile networks. Additionally, most video transmissions over LTE networksuse the Dynamic Adaptive Streaming over HTTP (DASH) protocol. Hence, this study assesses thebehavior of video service through DASH implementation based on fuzzy logic known as Fuzzy DASH(FDASH) over LTE networks, considering different numbers of users and handovers. This paper alsoaddresses various quality of service parameters such as delay, packet loss, packet loss variation, andperformance. Herein, FDASH is presented as a viable video service consumption option because itadapts streaming to the different conditions and requirements of LTE mobile wireless technology.El video streaming es el servicio que mayor porcentaje de tráfco genera con respecto altotal de datos en las redes móviles de evolución de largo plazo LTE (por sus siglas en inglés). Por otraparte, el protocolo de Streaming Adaptativo Dinámico sobre HTPP (DASH, por sus siglas en inglés)ha sido seleccionado para la transmisión del video en las redes LTE. Así, en este artículo, se presentael análisis del comportamiento del servicio de video mediante una implementación de DASH bajológica difusa denominada FDASH (fuzzy DASH) en una red LTE, considerando diferente número deusuarios y traspasos (handover). Se analizan parámetros de calidad de servicio, como retardo, pérdida de paquetes, variación de pérdida de paquetes y rendimiento. Mediante este trabajo, se presentaFDASH como opción para el consumo del servicio de video, ya que permite adaptarse a las diferentescondiciones y exigencias de la tecnología inalámbrica móvil LTE

Quality of experience and access network traffic management of HTTP adaptive video streaming

The thesis focuses on Quality of Experience (QoE) of HTTP adaptive video streaming (HAS) and traffic management in access networks to improve the QoE of HAS. First, the QoE impact of adaptation parameters and time on layer was investigated with subjective crowdsourcing studies. The results were used to compute a QoE-optimal adaptation strategy for given video and network conditions. This allows video service providers to develop and benchmark improved adaptation logics for HAS. Furthermore, the thesis investigated concepts to monitor video QoE on application and network layer, which can be used by network providers in the QoE-aware traffic management cycle. Moreover, an analytic and simulative performance evaluation of QoE-aware traffic management on a bottleneck link was conducted. Finally, the thesis investigated socially-aware traffic management for HAS via Wi-Fi offloading of mobile HAS flows. A model for the distribution of public Wi-Fi hotspots and a platform for socially-aware traffic management on private home routers was presented. A simulative performance evaluation investigated the impact of Wi-Fi offloading on the QoE and energy consumption of mobile HAS.Die Doktorarbeit beschäftigt sich mit Quality of Experience (QoE) – der subjektiv empfundenen Dienstgüte – von adaptivem HTTP Videostreaming (HAS) und mit Verkehrsmanagement, das in Zugangsnetzwerken eingesetzt werden kann, um die QoE des adaptiven Videostreamings zu verbessern. Zuerst wurde der Einfluss von Adaptionsparameters und der Zeit pro Qualitätsstufe auf die QoE von adaptivem Videostreaming mittels subjektiver Crowdsourcingstudien untersucht. Die Ergebnisse wurden benutzt, um die QoE-optimale Adaptionsstrategie für gegebene Videos und Netzwerkbedingungen zu berechnen. Dies ermöglicht Dienstanbietern von Videostreaming verbesserte Adaptionsstrategien für adaptives Videostreaming zu entwerfen und zu benchmarken. Weiterhin untersuchte die Arbeit Konzepte zum Überwachen von QoE von Videostreaming in der Applikation und im Netzwerk, die von Netzwerkbetreibern im Kreislauf des QoE-bewussten Verkehrsmanagements eingesetzt werden können. Außerdem wurde eine analytische und simulative Leistungsbewertung von QoE-bewusstem Verkehrsmanagement auf einer Engpassverbindung durchgeführt. Schließlich untersuchte diese Arbeit sozialbewusstes Verkehrsmanagement für adaptives Videostreaming mittels WLAN Offloading, also dem Auslagern von mobilen Videoflüssen über WLAN Netzwerke. Es wurde ein Modell für die Verteilung von öffentlichen WLAN Zugangspunkte und eine Plattform für sozialbewusstes Verkehrsmanagement auf privaten, häuslichen WLAN Routern vorgestellt. Abschließend untersuchte eine simulative Leistungsbewertung den Einfluss von WLAN Offloading auf die QoE und den Energieverbrauch von mobilem adaptivem Videostreaming