Improving Content Delivery Efficiency through Multi-Layer Mobile Edge Adaptation

This paper presents a novel architecture for optimizing the HTTP-based multimedia delivery in multi-user mobile networks. This proposal combines the usual client-driven dynamic adaptation scheme DASH-3GPP with network-assisted adaptation capabilities, in order to maximize the overall Quality of Experience. The foundation of this combined adaptation scheme is based on two state of the art technologies. On one hand, adaptive HTTP streaming with multi-layer encoding allows efficient media delivery and improves the experienced media quality in highly dynamic channels. Additionally, it enables the possibility to implement network-level adaptations for better coping with multi-user scenarios. On the other hand, mobile edge computing facilitates the deployment of mobile services close to the user. This approach brings new possibilities in modern and future mobile networks, such as close to zero delays and awareness of the radio status. The proposal in this paper introduces a novel element, denoted as Mobile Edge-DASH Adaptation Function, which combines all these advantages to support efficient media delivery in mobile multi-user scenarios. Furthermore, we evaluate the performance enhancements of this content- and user context-aware scheme through simulations of a mobile multimedia scenario.European Union H2020 programme: Grant Agreement H2020-ICT-671596. Spanish Ministerio de Economia y Competitividad (MINECO): grant TEC2013-46766-R

AngelCast: cloud-based peer-assisted live streaming using optimized multi-tree construction

Increasingly, commercial content providers (CPs) offer streaming solutions using peer-to-peer (P2P) architectures, which promises significant scalabil- ity by leveraging clients’ upstream capacity. A major limitation of P2P live streaming is that playout rates are constrained by clients’ upstream capac- ities – typically much lower than downstream capacities – which limit the quality of the delivered stream. To leverage P2P architectures without sacri- ficing quality, CPs must commit additional resources to complement clients’ resources. In this work, we propose a cloud-based service AngelCast that enables CPs to complement P2P streaming. By subscribing to AngelCast, a CP is able to deploy extra resources (angel), on-demand from the cloud, to maintain a desirable stream quality. Angels do not download the whole stream, nor are they in possession of it. Rather, angels only relay the minimal fraction of the stream necessary to achieve the desired quality. We provide a lower bound on the minimum angel capacity needed to maintain a desired client bit-rate, and develop a fluid model construction to achieve it. Realizing the limitations of the fluid model construction, we design a practical multi- tree construction that captures the spirit of the optimal construction, and avoids its limitations. We present a prototype implementation of AngelCast, along with experimental results confirming the feasibility of our service.Supported in part by NSF awards #0720604, #0735974, #0820138, #0952145, #1012798 #1012798 #1430145 #1414119. (0720604 - NSF; 0735974 - NSF; 0820138 - NSF; 0952145 - NSF; 1012798 - NSF; 1430145 - NSF; 1414119 - NSF

VANETs Multipath Video Data Streaming Considering Road Features

Multipath video streaming in Vehicular Ad-hoc Networks (VANETs) is an evolving research topic. The adoption of video transmission in VANETs communication has become essential due to the comprehensiveness and applicability of video data for on-road advertisement and infotainment. Meanwhile, several research studies have considered how to apply and improve the transmission of the video quality. Due to this, the concurrent multipath transmission has been employed in order to achieve load balancing and path diversity, because of the high data rate of the video data.  However, the main nature of the road, which is the pathway for VANET nodes has not been considered explicitly. In this paper, the road features are considered for VANETs multipath video streaming based on the greedy geographical routing protocol. Thus, VANETs Multipath Video Streaming based on Road Features (VMVS-RF) protocol has been proposed. The protocol was compared with an ordinary Multipath Video Streaming (MVS). The result demonstrates that the proposed VMVS-RF protocol outperforms the MVS in terms of Data Receiving Rate (DRR), Structural Similarity (SSIM) index and Packet Loss Ratio (PLR)

Video processing for panoramic streaming using HEVC and its scalable extensions

Panoramic streaming is a particular way of video streaming where an arbitrary Region-of-Interest (RoI) is transmitted from a high-spatial resolution video, i.e. a video covering a very “wide-angle” (much larger than the human field-of-view – e.g. 360°). Some transport schemes for panoramic video delivery have been proposed and demonstrated within the past decade, which allow users to navigate interactively within the high-resolution videos. With the recent advances of head mounted displays, consumers may soon have immersive and sufficiently convenient end devices at reach, which could lead to an increasing demand for panoramic video experiences. The solution proposed within this paper is built upon tile-based panoramic streaming, where users receive a set of tiles that match their RoI, and consists in a low-complexity compressed domain video processing technique for using H.265/HEVC and its scalable extensions (H.265/SHVC and H.265/MV-HEVC). The proposed technique generates a single video bitstream out of the selected tiles so that a single hardware decoder can be used. It overcomes the scalability issue of previous solutions not using tiles and the battery consumption issue inherent of tile-based panorama streaming, where multiple parallel software decoders are used. In addition, the described technique is capable of reducing peak streaming bitrate during changes of the RoI, which is crucial for allowing a truly immersive and low latency video experience. Besides, it makes it possible to use Open GOP structures without incurring any playback interruption at switching events, which provides a better compression efficiency compared to closed GOP structures

Guided Transcoding for Next-Generation Video Coding (HEVC)

Video content is the dominant traffic type on mobile networks today and this portion is only expected to increase in the future. In this thesis we investigate ways of reducing bit rates for adaptive streaming applications in the latest video coding standard, H.265 / High Efficiency Video Coding (HEVC). The current models for offering different-resolution versions of video content in a dynamic way, so called adaptive streaming, require either large amounts of storage capacity where full encodings of the material is kept at all times, or extremely high computational power in order to regenerate content on-demand. Guided transcoding aims at finding a middle-ground were we can store and transmit less data, at full or near-full quality, while still keeping computational complexity low. This is achieved by shifting the computationally heavy operations to a preprocessing step where so called side-information is generated. The side-information can then be used to quickly reconstruct sequences on-demand -- even when running on generic, non-specialized, hardware. Two method for generating side-information, pruning and deflation, are compared on a varying set of standardized HEVC test sequences and the respective upsides and downsides of each method are discussed.Genom att slänga bort viss information från en komprimerad video och sedan återskapa sekvensen i realtid kan vi minska behovet av lagringsutrymme för adaptiv videostreaming med 20–30%. Detta med helt bibehållen bildkvalité eller endast små försämringar. ==================== Adaptiv streaming Streaming är ett populärt sätt att skicka video över internet där en sekvens delas upp i korta segment som skickas kontinuerligt till användaren. Dessa segment kan skickas med varierande kvalité, och en modell där vi automatiskt känner av nätverkets belastning och dynamiskt anpassar kvalitén kallas för adaptiv streaming. Detta är ett system som används av SVT Play, TV4 Play och YouTube. HD- eller UltraHD-video måste komprimeras för att kunna skickas över ett nätverk – den tar helt enkelt för stor plats annars. Video som kodas med den senaste komprimeringsstandarden, HEVC/H.265, blir upp emot 700 gånger mindre med minimala försämringar av bildkvalitén. Ett segment på tio sekunder som tar 1,5 GB att skicka i rå form kan då komprimeras till strax över 2 MB. För att kunna erbjuda tittaren en videosekvens – en film eller ett TV-program – i varierande kvalité, skapar man olika kodningar av materialet. Generellt har vi inte möjlighet att förändra kvalitén på en sekvens i efterhand – omkodning av även en kort HD-video tar timmar att genomföra – så för att adaptiv streaming ska kunna fungera i praktiken genereras alla versioner på förhand och sparas undan. Men detta kräver stort lagringsutrymme. Guided transcoding Guided transcoding (”guidad omkodning”) erbjuder ett sätt att minska behovet av lagringsutrymme genom att slänga bort viss information och sedan återskapa den vid behov i ett senare skede. Vi gör detta för varje sekvens av lägre kvalité, men behåller högsta kvalitén som den är. En stympad lågkvalité-video tillsammans med videon av högsta kvalitén kan sedan användas för att exakt återskapa sekvensen. Denna process är mycket snabb i jämförelse med vanlig omkodning, så vi kan med kort varsel generera videokodningar av varierande kvalité. Vi har undersökt två metoder för plocka bort och återskapa videoinformation: pruning och deflation. Den första ger små försämringar i bildkvalitén och sparar närmare 30% lagringsutrymme. Den senare har ingen påverkan på bildkvalitén men sparar bara drygt 20% i utrymme

Radio resource allocation algorithms for multicast OFDM systems

Mención Internacional en el título de doctorVideo services have become highly demanded in mobile networks leading to an unprecedented traffic growth. It is expected that traffic from wireless and mobile devices will account for nearly 70 percent of total IP traffic by the year 2020, and the video services will account for nearly 75 percent of mobile data traffic by 2022. Multicast transmission is one of the key enablers towards a more spectral and energy efficient distribution of multimedia content in current and envisaged mobile networks. It is worth noting that multicast is a mechanism that efficiently delivers the same content to many users, not only focusing on video broadcasting, but also distributing many other media, such as software updates, weather forecast or breaking news. Although multicast services are available in Long Term Evolution (LTE) and LTE-Advanced (LTE-A) networks, new improvements are needed in some areas to handle the demands expected in the near future. Resource allocation techniques for multicast services are one of the main challenging issues, since it is required the development of novel schemes to meet the demands of their evolution towards the next generation. Most multicast techniques adopt rather conservative strategies that select a very robust modulation and coding scheme (MCS), whose characteristics are determined by the propagation conditions experienced by the worst user in the group in order to ensure that all users in a multicast group are able to correctly decode the received data. Obviously, this robustness comes at the prize of a low spectral efficiency. This thesis presents an exhaustive study of broadcast/multicast technology for current mobile networks, especially focusing on the scheduling and resource allocation (SRA) strategies to maximize the potential benefits that multicast transmissions imply on the spectral efficiency. Based on that issue, some contributions have been made to the state of the art in the radio resource management (RRM) for current and beyond mobile multicast services. • In the frame of LTE/LTE-A, the evolved multimedia broadcast and multicast service (eMBMS) shares the physical layer resources with the unicast transmission mode (at least up to Release 12). Consequently, the time allocation to multicast transmission is limited to a maximum of a 60 percent, and the remaining subframes (at least 40 percent) are reserved for unicast transmissions. With the aim of achieving the maximum aggregated data rate (ADR) among the multicast users, we have implemented several innovative SRA schemes that combine the allocation of multicast and unicast resources in the LTE/LTE-A frame, guaranteeing the prescribed quality of service (QoS) requirements for every user. • In the specific context of wideband communication systems, the selection of the multicast MCS has often relied on the use of wideband channel quality indicators (CQIs), providing rather imprecise information regarding the potential capacity of the multicast channel. Only recently has the per-subband CQI been used to improve the spectral efficiency of the system without compromising the link robustness. We have proposed novel subband CQI-based multicast SRA strategies that, relying on the selection of more spectrally efficient transmission modes, lead to increased data rates while still being able to fulfill prescribed QoS metrics. • Mobile broadcast/multicast video services require effective and low complexity SRA strategies. We have proposed an SRA strategy based on multicast subgrouping and the scalable video coding (SVC) technique for multicast video delivery. This scheme focuses on reducing the search space of solutions and optimizes the ADR. The results in terms of ADR, spectral efficiency, and fairness among multicast users, along with the low complexity of the algorithm, show that this new scheme is adequate for real systems. These contributions are intended to serve as a reference that motivate ongoing and future investigation in the challenging field of RRM for broadcast/ multicast services in next generation mobile networks.La demanda de servicios de vídeo en las redes móviles ha sufrido un incremento exponencial en los últimos años, lo que a su vez ha desembocado en un aumento sin precedentes del tráfico de datos. Se espera que antes del año 2020, el trafico debido a dispositivos móviles alcance cerca del 70 por ciento del tráfico IP total, mientras que se prevé que los servicios de vídeo sean prácticamente el 75 por ciento del tráfico de datos en las redes móviles hacia el 2022. Las transmisiones multicast son una de las tecnologías clave para conseguir una distribución más eficiente, tanto espectral como energéticamente, del contenido multimedia en las redes móviles actuales y futuras. Merece la pena reseñar que el multicast es un mecanismo de entrega del mismo contenido a muchos usuarios, que no se enfoca exclusivamente en la distribución de vídeo, sino que también permite la distribución de otros muchos contenidos, como actualizaciones software, información meteorológica o noticias de última hora. A pesar de que los servicios multicast ya se encuentran disponibles en las redes Long Term Evolution (LTE) y LTE-Advanced (LTE-A), la mejora en algunos ámbitos resulta necesaria para manejar las demandas que se prevén a corto plazo. Las técnicas de asignación de recursos para los servicios multicast suponen uno de los mayores desafíos, ya que es necesario el desarrollo de nuevos esquemas que nos permitan acometer las exigencias que supone su evolución hacia la próxima generación. La mayor parte de las técnicas multicast adoptan estrategias conservadoras, seleccionando esquemas de modulación y codificación (MCS) impuestos por las condiciones de propagación que experimenta el usuario del grupo con peor canal, para así asegurar que todos los usuarios pertenecientes al grupo multicast sean capaces de decodificar correctamente los datos recibidos. Como resulta obvio, la utilización de esquemas tan robustos conlleva el precio de sufrir una baja eficiencia espectral. Esta tesis presenta un exhaustivo estudio de la tecnología broadcast/ multicast para las redes móviles actuales, que se centra especialmente en las estrategias de asignación de recursos (SRA), cuyo objetivo es maximizar los beneficios que la utilización de transmisiones multicast potencialmente implica en términos de eficiencia espectral. A partir de dicho estudio, hemos realizado varias contribuciones al estado del arte en el ámbito de la gestión de recursos radio (RRM) para los servicios multicast, aplicables en las redes móviles actuales y futuras. • En el marco de LTE/LTE-A, el eMBMS comparte los recursos de la capa física con las transmisiones unicast (al menos hasta la revisión 12). Por lo tanto, la disponibilidad temporal de las transmisiones multicast está limitada a un máximo del 60 por ciento, reservándose las subtramas restantes (al menos el 40 por ciento) para las transmisiones unicast. Con el objetivo de alcanzar la máxima tasa total de datos (ADR) entre los usuarios multicast, hemos implementado varios esquemas innovadores de SRA que combinan la asignación de los recursos multicast y unicast de la trama LTE/LTE-A, garantizando los requisitos de QoS a cada usuario. • En los sistemas de comunicaciones de banda ancha, la selección del MCS para transmisiones multicast se basa habitualmente en la utilización de CQIs de banda ancha, lo que proporciona información bastante imprecisa acerca de la capacidad potencial del canal multicast. Recientemente se ha empezado a utilizar el CQI por subbanda para mejorar la eficiencia espectral del sistema sin comprometer la robustez de los enlaces. Hemos propuesto nuevas estrategias para SRA multicast basadas en el CQI por subbanda que, basándose en la selección de los modos de transmisión con mayor eficiencia espectral, conducen a mejores tasas de datos, a la vez que permiten cumplir los requisitos de QoS. • Los servicios móviles de vídeo broadcast/multicast precisan estrategias eficientes de SRA con baja complejidad. Hemos propuesto una estrategia de SRA basada en subgrupos multicast y la técnica de codificación de vídeo escalable (SVC) para la difusión de vídeo multicast, la cual se centra en reducir el espacio de búsqueda de soluciones y optimizar el ADR. Los resultados obtenidos en términos de ADR, eficiencia espectral y equidad entre los usuarios multicast, junto con la baja complejidad del algoritmo, ponen de manifiesto que el esquema propuesto es adecuado para su implantación en sistemas reales. Estas contribuciones pretenden servir de referencia que motive la investigación actual y futura en el interesante ámbito de RRM para los servicios broadcast/multicast en las redes móviles de próxima generación.Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Atilio Manuel Da Silva Gameiro.- Secretario: Víctor Pedro Gil Jiménez.- Vocal: María de Diego Antó

Assessing Feature Representations for Instance-Based Cross-Domain Anomaly Detection in Cloud Services Univariate Time Series Data

In this paper, we compare and assess the efficacy of a number of time-series instance feature representations for anomaly detection. To assess whether there are statistically significant differences between different feature representations for anomaly detection in a time series, we calculate and compare confidence intervals on the average performance of different feature sets across a number of different model types and cross-domain time-series datasets. Our results indicate that the catch22 time-series feature set augmented with features based on rolling mean and variance performs best on average, and that the difference in performance between this feature set and the next best feature set is statistically significant. Furthermore, our analysis of the features used by the most successful model indicates that features related to mean and variance are the most informative for anomaly detection. We also find that features based on model forecast errors are useful for anomaly detection for some but not all datasets

Assessing Feature Representations for Instance-Based Cross-Domain Anomaly Detection in Cloud Services Univariate Time Series Data

In this paper, we compare and assess the efficacy of a number of time-series instance feature representations for anomaly detection. To assess whether there are statistically significant differences between different feature representations for anomaly detection in a time series, we calculate and compare confidence intervals on the average performance of different feature sets across a number of different model types and cross-domain time-series datasets. Our results indicate that the catch22 time-series feature set augmented with features based on rolling mean and variance performs best on average, and that the difference in performance between this feature set and the next best feature set is statistically significant. Furthermore, our analysis of the features used by the most successful model indicates that features related to mean and variance are the most informative for anomaly detection. We also find that features based on model forecast errors are useful for anomaly detection for some but not all datasets