Subjective quality assessment of an adaptive video streaming model

With the recent increased popularity and high usage of HTTP Adaptive Streaming (HAS) techniques, various studies have been carried out in this area which generally focused on the technical enhancement of HAS technology and applications. However, a lack of common HAS standard led to multiple proprietary approaches which have been developed by major Internet companies. In the emerging MPEG-DASH standard the packagings of the video content and HTTP syntax have been standardized; but all the details of the adaptation behavior are left to the client implementation. Nevertheless, to design an adaptation algorithm which optimizes the viewing experience of the enduser, the multimedia service providers need to know about the Quality of Experience (QoE) of different adaptation schemes. Taking this into account, the objective of this experiment was to study the QoE of a HAS-based video broadcast model. The experiment has been carried out through a subjective study of the end user response to various possible clients’ behavior for changing the video quality taking different QoE-influence factors into account. The experimental conclusions have made a good insight into the QoE of different adaptation schemes which can be exploited by HAS clients for designing the adaptation algorithms.

Crowdsourcing based subjective quality assessment of adaptive video streaming

In order to cater for user's quality of experience (QoE) requirements, HTTP adaptive streaming (HAS) based solutions of video services have become popular recently. User QoE feedback can be instrumental in improving the capabilities of such services. Perceptual quality experiments that involve humans are considered to be the most valid method of the assessment of QoE. Besides lab-based subjective experiments, crowdsourcing based subjective assessment of video quality is gaining popularity as an alternative method. This paper presents insights into a study that investigates perceptual preferences of various adaptive video streaming scenarios through crowdsourcing based subjective quality assessment.

Quality of experience of adaptive video streaming: investigation in service parameters and subjective quality assessment methodology

The usage of HTTP adaptive streaming (HAS) has become widely spread in multimedia services. Because it allows the service providers to improve the network resource utilization and user׳s Quality of Experience (QoE). Using this technology, the video playback interruption is reduced since the network and server status in addition to capability of user device, all are taken into account by HAS client to adapt the quality to the current condition. Adaptation can be done using different strategies. In order to provide optimal QoE, the perceptual impact of adaptation strategies from point of view of the user should be studied. However, the time-varying video quality due to the adaptation which usually takes place in a long interval introduces a new type of impairment making the subjective evaluation of adaptive streaming system challenging. The contribution of this paper is two-fold: first, it investigates the testing methodology to evaluate HAS QoE by comparing the subjective experimental outcomes obtained from ACR standardized method and a semi-continuous method developed to evaluate the long sequences. In addition, influence of using audiovisual stimuli to evaluate the video-related impairment is inquired. Second, impact of some of the adaptation technical factors including the quality switching amplitude and chunk size in combination with high range of commercial content type is investigated. The results of this study provide a good insight toward achieving appropriate testing method to evaluate HAS QoE, in addition to designing switching strategies with optimal visual quality.

A quality driven framework for adaptive video streaming in mobile wireless networks

The concern over the quality of delivering video streaming services in mobile wireless networks is addressed in this work. A framework that enhances the Quality of Experience (QoE) of end users through a quality driven resource allocation scheme is proposed. To play a key role, an objective no-reference quality metric, Pause Intensity (PI), is adopted to derive a resource allocation algorithm for video streaming. The framework is examined in the context of 3GPP Long Term Evolution (LTE) systems. The requirements and structure of the proposed PI-based framework are discussed, and results are compared with existing scheduling methods on fairness, efficiency and correlation (between the required and allocated data rates). Furthermore, it is shown that the proposed framework can produce a trade-off between the three parameters through the QoE-aware resource allocation process.

ALD: adaptive layer distribution for scalable video

Bandwidth constriction and datagram loss are prominent issues that affect the perceived quality of streaming video over lossy networks, such as wireless. The use of layered video coding seems attractive as a means to alleviate these issues, but its adoption has been held back in large part by the inherent priority assigned to the critical lower layers and the consequences for quality that result from their loss. The proposed use of forward error correction (FEC) as a solution only further burdens the bandwidth availability and can negate the perceived benefits of increased stream quality. In this paper, we propose Adaptive Layer Distribution (ALD) as a novel scalable media delivery technique that optimises the tradeoff between the streaming bandwidth and error resiliency. ALD is based on the principle of layer distribution, in which the critical stream data is spread amongst all datagrams thus lessening the impact on quality due to network losses. Additionally, ALD provides a parameterised mechanism for dynamic adaptation of the scalable video, while providing increased resilience to the highest quality layers. Our experimental results show that ALD improves the perceived quality and also reduces the bandwidth demand by up to 36% in comparison to the well-known Multiple Description Coding (MDC) technique.

Subjective QoE Analysis of HTTP Adaptive Streaming Applications

Actualmente la optimization de la calidad de experiencia (Quality of Experience- QoE) de HTTP Adaptive Streaming (HAS) de video recibe una atención creciente. Este incremento de interés proviene fundamentalmente de las carencias de las soluciones actuales HAS, que, al no ser QoE-driven, no incluyen la percepción de la calidad de los usuarios finales como una parte integral de la lógica de adaptación. Por lo tanto, la obtención de información de referencia fiable en QoE en HAS presenta retos importantes, ya que las metodologías de evaluación subjetiva de la calidad de vídeo propuestas en las normas actuales no son adecuadas para tratar con la variación temporal de la calidad que es consustancial de HAS. Esta tesis investiga la influencia de la adaptación dinámica en la calidad de la transmisión de vídeo considerando métodos de evaluación subjetiva. Tras un estudio exhaustivo del estado del arte en la evaluación subjetiva de QoE en HAS, se han resaltado los retos asociados y las líneas de investigación abiertas. Como resultado, se han seleccionado dos líneas principales de investigación: el análisis del impacto en la QoE de los parámetros de las técnicas de adaptación y la investigación de las metodologías de prueba subjetiva adecuada para evaluación de QoE en HAS. Se han llevado a cabo un conjunto de experimentos de laboratorio para investigar las cuestiones planteadas mediante la utilización de diferentes metodologáas para pruebas subjetivas. El análisis estadístico muestra que no son robustas todas las suposiciones y reivindicaciones de las referencias analizadas, en particular en lo que respecta al impacto en la QoE de la frecuencia de las variaciones de calidad, de las adaptaciones suaves o abruptas y de las oscilaciones de calidad. Por otra parte, nuestros resultados confirman la influencia de otros parámetros, como la longitud de los segmentos de vídeo y la amplitud de las oscilaciones de calidad. Los resultados también muestran que tomar en consideración las características objetivas de los contenidos puede ser beneficioso para la mejora de la QoE en HAS. Además, todos los resultados han sido validados mediante extensos análisis experimentales que han incluido estudio tanto en otros laboratorios como en crowdsourcing Por último, sobre los aspectos metodológicos de las pruebas subjetivas de QoE, se ha realizado la comparación entre los resultados experimentales obtenidos a partir de un método estandarizado basado en estímulos cortos (ACR) y un método semi continuo (desarrollado para la evaluación de secuencias prolongadas de vídeo). A pesar de algunas diferencias, el resultado de los análisis estadísticos no muestra ningún efecto significativo de la metodología de prueba. Asimismo, aunque se percibe la influencia de la presencia de audio en la evaluación de degradaciones del vídeo, no se han encontrado efectos estadísticamente significativos de dicha presencia. A partir de la ausencia de influencia del método de prueba y de la presencia de audio, se ha realizado un análisis adicional sobre el impacto de realizar comparaciones estadísticas múltiples en niveles estadísticos de importancia que aumentan la probabilidad de los errores de tipo-I (falsos positivos). Nuestros resultados muestran que, para obtener un efectos sólido en el análisis estadístico de los resultados subjetivos, es necesario aumentar el número de sujetos de las pruebas claramente por encima de los tamaños de muestras propuestos por las normas y recomendaciones actuales. ABSTRACT Optimizing the Quality of Experience (QoE) of HTTP adaptive video streaming (HAS) is receiving increasing attention nowadays. The growth of interest is mainly caused by the fact that current HAS solutions are not QoE-driven, i.e. end-user quality perception is not integral part of the adaptation logic. However, obtaining the necessary reliable ground truths on HAS QoE faces substantial challenges, since the subjective video quality assessment methodologies as proposed by current standards are not well-suited for dealing with the time-varying quality properties that are characteristic for HAS. This thesis investigates the influence of dynamic quality adaptation on the QoE of streaming video by means of subjective evaluation approaches. Based on a comprehensive survey of related work on subjective HAS QoE assessment, the related challenges and open research questions are highlighted and discussed. As a result, two main research directions are selected for further investigation: analysis of the QoE impact of different technical adaptation parameters, and investigation of testing methodologies suitable for HAS QoE evaluation. In order to investigate related research issues and questions, a set of laboratory experiments have been conducted using different subjective testing methodologies. Our statistical analysis demonstrates that not all assumptions and claims reported in the literature are robust, particularly as regards the QoE impact of switching frequency, smooth vs. abrupt switching, and quality oscillation. On the other hand, our results confirm the influence of some other parameters such as chunk length and switching amplitude on perceived quality. We also show that taking the objective characteristics of the content into account can be beneficial to improve the adaptation viewing experience. In addition, all aforementioned findings are validated by means of an extensive cross-experimental analysis that involves external laboratory and crowdsourcing studies. Finally, to address the methodological aspects of subjective QoE testing, a comparison between the experimental results obtained from a (short stimuli-based) ACR standardized method and a semi-continuous method (developed for assessment of long video sequences) has been performed. In spite of observation of some differences, the result of statistical analysis does not show any significant effect of testing methodology. Similarly, although the influence of audio presence on evaluation of video-related degradations is perceived, no statistically significant effect of audio presence could be found. Motivating by this finding (no effect of testing method and audio presence), a subsequent analysis has been performed investigating the impact of performing multiple statistical comparisons on statistical levels of significance which increase the likelihood of Type-I errors (false positives). Our results show that in order to obtain a strong effect from the statistical analysis of the subjective results, it is necessary to increase the number of test subjects well beyond the sample sizes proposed by current quality assessment standards and recommendations.

Quality-driven resource utilization methods for video streaming in wireless communication networks

This research is focused on the optimisation of resource utilisation in wireless mobile networks with the consideration of the users’ experienced quality of video streaming services. The study specifically considers the new generation of mobile communication networks, i.e. 4G-LTE, as the main research context. The background study provides an overview of the main properties of the relevant technologies investigated. These include video streaming protocols and networks, video service quality assessment methods, the infrastructure and related functionalities of LTE, and resource allocation algorithms in mobile communication systems. A mathematical model based on an objective and no-reference quality assessment metric for video streaming, namely Pause Intensity, is developed in this work for the evaluation of the continuity of streaming services. The analytical model is verified by extensive simulation and subjective testing on the joint impairment effects of the pause duration and pause frequency. Various types of the video contents and different levels of the impairments have been used in the process of validation tests. It has been shown that Pause Intensity is closely correlated with the subjective quality measurement in terms of the Mean Opinion Score and this correlation property is content independent. Based on the Pause Intensity metric, an optimised resource allocation approach is proposed for the given user requirements, communication system specifications and network performances. This approach concerns both system efficiency and fairness when establishing appropriate resource allocation algorithms, together with the consideration of the correlation between the required and allocated data rates per user. Pause Intensity plays a key role here, representing the required level of Quality of Experience (QoE) to ensure the best balance between system efficiency and fairness. The 3GPP Long Term Evolution (LTE) system is used as the main application environment where the proposed research framework is examined and the results are compared with existing scheduling methods on the achievable fairness, efficiency and correlation. Adaptive video streaming technologies are also investigated and combined with our initiatives on determining the distribution of QoE performance across the network. The resulting scheduling process is controlled through the prioritization of users by considering their perceived quality for the services received. Meanwhile, a trade-off between fairness and efficiency is maintained through an online adjustment of the scheduler’s parameters. Furthermore, Pause Intensity is applied to act as a regulator to realise the rate adaptation function during the end user’s playback of the adaptive streaming service. The adaptive rates under various channel conditions and the shape of the QoE distribution amongst the users for different scheduling policies have been demonstrated in the context of LTE. Finally, the work for interworking between mobile communication system at the macro-cell level and the different deployments of WiFi technologies throughout the macro-cell is presented. A QoEdriven approach is proposed to analyse the offloading mechanism of the user’s data (e.g. video traffic) while the new rate distribution algorithm reshapes the network capacity across the macrocell. The scheduling policy derived is used to regulate the performance of the resource allocation across the fair-efficient spectrum. The associated offloading mechanism can properly control the number of the users within the coverages of the macro-cell base station and each of the WiFi access points involved. The performance of the non-seamless and user-controlled mobile traffic offloading (through the mobile WiFi devices) has been evaluated and compared with that of the standard operator-controlled WiFi hotspots.

Ensuring QoE in contemporary mobile networks for video content distribution

A framework that aims to best utilize the mobile network resources for video applications is presented in this paper. The main contribution of the work proposed is the QoE-driven optimization method that can maintain a desired trade-off between fairness and efficiency in allocating resources in terms of data rates to video streaming users in LTE networks. This method is concerned with the control of the user satisfaction level from the service continuity's point of view and applies appropriate QoE metrics (Pause Intensity and variations) to determine the scheduling strategies in combination with the mechanisms used for adaptive video streaming such as 3GP/MPEG-DASH. The superiority of the proposed algorithms are demonstrated, showing how the resources of a mobile network can be optimally utilized by using quantifiable QoE measurements. This approach can also find the best match between demand and supply in the process of network resource distribution.

Efficient delivery of scalable media streaming over lossy networks

Recent years have witnessed a rapid growth in the demand for streaming video over the Internet, exposing challenges in coping with heterogeneous device capabilities and varying network throughput. When we couple this rise in streaming with the growing number of portable devices (smart phones, tablets, laptops) we see an ever-increasing demand for high-definition videos online while on the move. Wireless networks are inherently characterised by restricted shared bandwidth and relatively high error loss rates, thus presenting a challenge for the efficient delivery of high quality video. Additionally, mobile devices can support/demand a range of video resolutions and qualities. This demand for mobile streaming highlights the need for adaptive video streaming schemes that can adjust to available bandwidth and heterogeneity, and can provide us with graceful changes in video quality, all while respecting our viewing satisfaction. In this context the use of well-known scalable media streaming techniques, commonly known as scalable coding, is an attractive solution and the focus of this thesis. In this thesis we investigate the transmission of existing scalable video models over a lossy network and determine how the variation in viewable quality is affected by packet loss. This work focuses on leveraging the benefits of scalable media, while reducing the effects of data loss on achievable video quality. The overall approach is focused on the strategic packetisation of the underlying scalable video and how to best utilise error resiliency to maximise viewable quality. In particular, we examine the manner in which scalable video is packetised for transmission over lossy networks and propose new techniques that reduce the impact of packet loss on scalable video by selectively choosing how to packetise the data and which data to transmit. We also exploit redundancy techniques, such as error resiliency, to enhance the stream quality by ensuring a smooth play-out with fewer changes in achievable video quality. The contributions of this thesis are in the creation of new segmentation and encapsulation techniques which increase the viewable quality of existing scalable models by fragmenting and re-allocating the video sub-streams based on user requirements, available bandwidth and variations in loss rates. We offer new packetisation techniques which reduce the effects of packet loss on viewable quality by leveraging the increase in the number of frames per group of pictures (GOP) and by providing equality of data in every packet transmitted per GOP. These provide novel mechanisms for packetizing and error resiliency, as well as providing new applications for existing techniques such as Interleaving and Priority Encoded Transmission. We also introduce three new scalable coding models, which offer a balance between transmission cost and the consistency of viewable quality.

Implementazione e analisi di algoritmi dinamici per trasmissione MPEG-DASH su client Android

Attualmente, la maggior parte dei dati che transitano sulla rete appartiene a contenuti multimediali. Più nello specifico, è lo Streaming Video ad avere la predominanza nella condivisione di Internet; vista la crescita che tale servizio ha subìto negli ultimi anni, si sono susseguiti diversi studi volti allo sviluppo di tecniche e metodologie che potessero migliorarlo. Una di queste è sicuramente l'Adaptive Video Streaming, tecnica utilizzata per garantire all'utente una buona Quality of Experience (QoE) mediante l'utilizzo dei cosiddetti "algoritmi di rate adaptation". Il lavoro svolto in questi studi si è voluto concentrare su due filoni distinti, ma allo stesso tempo confrontabili: la prima parte della tesi riguarda lo sviluppo e l'analisi di alcuni algoritmi di rate adaptation per DASH, mentre la seconda è relativa all'implementazione di un nuovo algoritmo che li possa affiancare, migliorando la QoE nel monitorare lo stato della connessione. Si è quindi dovuta implementare un'applicazione Android per lo streaming video, che fosse conforme allo standard MPEG-DASH e potesse fornire le informazioni di testing da utilizzare per le analisi. La tesi è suddivisa in quattro capitoli: il primo introduce l'argomento e definisce la terminologia necessaria alla comprensione degli studi; il secondo descrive alcuni dei lavori correlati allo streaming adattivo e introduce i due filoni principali della tesi, ovvero gli algoritmi di rate adaptation e la proposta di algoritmo per la selezione dinamica del segmento; il terzo presenta l'app SSDash, utilizzata come mezzo per le analisi sperimentali; infine, il quarto ed ultimo capitolo mostra i risultati delle analisi e le corrispondenti valutazioni.

An adaptive decomposition method for video

Scalable video coding (SVC) is an emerging standard built on the success of advanced video coding standard (H.264/AVC) by the Joint video team (JVT). Motion compensated temporal filtering (MCTF) and Closed loop hierarchical B pictures (CHBP) are two important coding methods proposed during initial stages of standardization. Either of the coding methods, MCTF/CHBP performs better depending upon noise content and characteristics of the sequence. This work identifies other characteristics of the sequences for which performance of MCTF is superior to that of CHBP and presents a method to adaptively select either of MCTF and CHBP coding methods at the GOP level. This method, referred as "Adaptive Decomposition" is shown to provide better R-D performance than of that by using MCTF or CRBP only. Further this method is extended to non-scalable coders.

BitBox!: A case study interface for teaching real-time adaptive music composition for video games

Real-time adaptive music is now well-established as a popular medium, largely through its use in video game soundtracks. Commercial packages, such as fmod, make freely available the underlying technical methods for use in educational contexts, making adaptive music technologies accessible to students. Writing adaptive music, however, presents a significant learning challenge, not least because it requires a different mode of thought, and tutor and learner may have few mutual points of connection in discovering and understanding the musical drivers, relationships and structures in these works. This article discusses the creation of ‘BitBox!’, a gestural music interface designed to deconstruct and explain the component elements of adaptive composition through interactive play. The interface was displayed at the Dare Protoplay games exposition in Dundee in August 2014. The initial proof-of- concept study proved successful, suggesting possible refinements in design and a broader range of applications.

ALD: adaptive layer distribution for scalable video

Recent years have witnessed a rapid growth in the demand for streaming video over the Internet and mobile networks, exposes challenges in coping with heterogeneous devices and varying network throughput. Adaptive schemes, such as scalable video coding, are an attractive solution but fare badly in the presence of packet losses. Techniques that use description-based streaming models, such as multiple description coding (MDC), are more suitable for lossy networks, and can mitigate the effects of packet loss by increasing the error resilience of the encoded stream, but with an increased transmission byte cost. In this paper, we present our adaptive scalable streaming technique adaptive layer distribution (ALD). ALD is a novel scalable media delivery technique that optimises the tradeoff between streaming bandwidth and error resiliency. ALD is based on the principle of layer distribution, in which the critical stream data are spread amongst all packets, thus lessening the impact on quality due to network losses. Additionally, ALD provides a parameterised mechanism for dynamic adaptation of the resiliency of the scalable video. The Subjective testing results illustrate that our techniques and models were able to provide levels of consistent high-quality viewing, with lower transmission cost, relative to MDC, irrespective of clip type. This highlights the benefits of selective packetisation in addition to intuitive encoding and transmission.

Adaptive temporal compressive sensing for video

This paper introduces the concept of adaptive temporal compressive sensing (CS) for video. We propose a CS algorithm to adapt the compression ratio based on the scene's temporal complexity, computed from the compressed data, without compromising the quality of the reconstructed video. The temporal adaptivity is manifested by manipulating the integration time of the camera, opening the possibility to realtime implementation. The proposed algorithm is a generalized temporal CS approach that can be incorporated with a diverse set of existing hardware systems. © 2013 IEEE.