Alisa Devlic

Viewport-adaptive navigable 360-degree video delivery

The delivery and display of 360-degree videos on Head-Mounted Displays (HMDs) presents many technical challenges. 360-degree videos are ultra high resolution spherical videos, which contain an omnidirectional view of the scene. However only a portion of this scene is displayed on the HMD. Moreover, HMD need to respond in 10 ms to head movements, which prevents the server to send only the displayed video part based on client feedback. To reduce the bandwidth waste, while still providing an immersive experience, a viewport-adaptive 360-degree video streaming system is proposed. The server prepares multiple video representations, which differ not only by their bit-rate, but also by the qualities of different scene regions. The client chooses a representation for the next segment such that its bit-rate fits the available throughput and a full quality region matches its viewing. We investigate the impact of various spherical-to-plane projections and quality arrangements on the video quality displayed to the user, showing that the cube map layout offers the best quality for the given bit-rate budget. An evaluation with a dataset of users navigating 360-degree videos demonstrates that segments need to be short enough to enable frequent view switches.

Context inference of users' social relationships and distributed policy management

Inference of high-level context is becoming crucial in development of context-aware applications. An example is social context inference - i.e., deriving social relations based upon the users daily communication with other people. The efficiency of this mechanism mainly depends on the method(s) used to draw inferences based on existing evidence and sample information, such as a training data. Our approach uses rule-based data mining, Bayesian network inference, and user feedback to compute the probabilities of another user being in the specific social relationship with a user whose daily communication is logged by a mobile phone. In addition, a privacy mechanism is required to ensure the users personal integrity and privacy when sharing this users sensitive context data. Therefore, the derived social relations are used to define a users policies for context access control, which grant the restricted context information scope depending on the users current context. Finally, we propose a distributed architecture capable of managing this context information based upon these context access policies.

A Use-Case Based Analysis of Network Management Functions in the ONF SDN Model

The concept of software-defined networking (SDN) recently gained huge momentum in the industry, driven mainly by IT companies interested in data center applications. In this paper, however, we apply SDN to the carrier domain, which poses additional requirements in terms of network management functions. As a specific use-case we take a virtualized carrier network shared by multiple customers. We consider the current SDN model as defined by the Open Networking Foundation (ONF), including the OpenFlow and OF-config protocols. Through a step-by-step discussion of the rocedures required to configure and manage the virtualized network, we analyze the applicability of the current SDN model as specified by the ONF. As a result, we identify shortcomings and propose necessary extensions to the ONF SDN model. The highlighted extensions include control network bootstrapping considerations, updates to the SDN and NOS model, and most importantly extensions of the OF-config management data model.

Context Grouping Mechanism for Context Distribution in Ubiquitous Environments

Context distribution is a key aspect for successful applications within mobile and ubiquitous computing environments. In such environments, context information is acquired by several and multiple context sensors distributed over the environment. Applications collect and react to these data, according to predefined adaptation mechanisms. The success of these mechanisms depends on the availability of context information, which is disseminated over the network. However, in practice, only a fraction of the observable context information is required by the adaptation mechanisms. Moreover, for privacy reasons, it is important to delimitate a scope for context dissemination. In this work we address these issues by proposing a context grouping mechanism which allows the definition of groups based on the context characteristics. Each group is defined by these characteristics and delimitate a given context information set that can be distributed among group members. This approach of context grouping acts as a two-fold mechanism. On the one hand, it controls and organizes context distribution over a peer-to-peer network. On the other hand, it proposes a primary and low-level privacy mechanism for context distribution, which is an important aspect influencing context distribution.

Location-aware information services using user profile matching

User profiles are used to integrate contextual information about mobile users and objects/devices in their environment. They represent structured knowledge used by a system to provide relevant information set to the user.

Optimal Set of 360-Degree Videos for Viewport-Adaptive Streaming

With the decreasing price of Head-Mounted Displays (HMDs), 360-degree videos are becoming popular. The streaming of such videos through the Internet with state of the art streaming architectures requires, to provide high immersion feeling, much more bandwidth than the median users access bandwidth. To decrease the need for bandwidth consumption while providing high immersion to users, scientists and specialists proposed to prepare and encode 360-degree videos into quality-variable video versions and to implement viewport-adaptive streaming. Quality-variable versions are different versions of the same video with non-uniformly spread quality: there exists some so-called Quality Emphasized Regions (QERs). With viewport-adaptive streaming the client, based on head movement prediction, downloads the video version with the high quality region closer to where the user will watch. In this paper we propose a generic theoretical model to find out the optimal set of quality-variable video versions based on traces of head positions of users watching a 360-degree video. We propose extensions to adapt the model to popular quality-variable version implementations such as tiling and offset projection. We then solve a simplified version of the model with two quality levels and restricted shapes for the QER. With this simplified model, we show that an optimal set of four quality-variable video versions prepared by a streaming server, together with a perfect head movement prediction, allow for 45% bandwidth savings to display video with the same average quality as state of the art solutions or allows an increase of 102% of the displayed quality for the same bandwidth budget.

SIP-based context distribution: does aggregation pay off?

Context-aware applications need quickly access to current context information, in order to adapt their behavior before this context changes. To achieve this, the context distribution mechanism has to timely discover context sources that can provide a particular context type, then acquire and distribute context information from these sources to the applications that requested this type of information. This paper reviews the state-of-the-art context distribution mechanisms according to identified requirements, then introduces a resource list-based subscription/notification mechanism for context sharing. This SIP-based mechanism enables subscriptions to a resource list containing URIs of multiple context sources that can provide the same context type and delivery of aggregated notifications containing context updates from each of these sources. Aggregation of context is thought to be important as it reduces the network traffic between entities involved in context distribution. However, it introduces an additional delay due to waiting for context updates and their aggregation. To investigate if this aggregation actually pays off, we measured and compared the time needed by an application to receive context updates after subscribing to a particular resource list (using RLS) versus after subscribing to each of the individual context sources (using SIMPLE) for different numbers of context sources. Our results show that RLS aggregation outperforms the SIMPLE presence mechanism with 3 or more context sources, regardless of their context updates size. Database performance was identified as a major bottleneck during aggregation, hence we used in-memory tables & prepared statements, leading to up to 57% database time improvement, resulting in a reduction of the aggregation time by up to 34%. With this reduction and an increase in context size, we pushed the aggregation payoff threshold closer to 2 context sources.

Synthesizing Context for a Sports Domain on a Mobile Device

In ubiquitous computing environments there are an increasing number and variety of devices that can generate context data. The challenge is to timely acquire, process, and deliver these data to context-aware applications. The role of context synthesis is to generate new knowledge, as a result of a reasoning process applied to context information that is already present in the system. The success of this mechanism mainly depends on the response time that the end-user or an application must wait for the response to a context query. This paper describes and evaluates an approach to context synthesis on a mobile device to be used by a set of applications in a sports domain. A scenario based on a live race at the Super Prestige Cyclocross in Gieten, Netherlands demonstrates the use of context synthesis to dynamically compose gaps and groups of cyclists in order to provide a nearly real-time virtual ranking service.

Energy Consumption Reduction via Context-Aware Mobile Video Pre-fetching

The arrival of smart phones and tablets, along with a flat rate mobile Internet pricing model have caused increasing adoption of mobile data services. According to recent studies, video has been the main driver of mobile data consumption, having a higher growth rate than any other mobile application. However, streaming a medium/high quality video files can be an issue in a mobile environment where available capacity needs to be shared among a large number of users. Additionally, the energy consumption in mobile devices increases proportionally with the duration of data transfers, which depend on the download data rates achievable by the device. In this respect, adoption of opportunistic content pre-fetching schemes that exploit times and locations with high data rates to deliver content before a user requests it, has the potential to reduce the energy consumption associated with content delivery and improve the users quality of experience, by allowing playback of pre-stored content with virtually no perceived interruptions or delays. This paper presents a family of opportunistic content pre-fetching schemes and compares their performance to standard on-demand access to content. By adopting a simulation approach on experimental data, collected with monitoring software installed in mobile terminals, we show that content pre-fetching can reduce energy consumption of the mobile devices by up to 30% when compared to the on demand download of the same file, with a time window of 1 hour needed to complete the content prepositioning.

A Context-Aware Privacy Policy Language for Controlling Access to Context Information of Mobile Users

This paper introduces a Context-aware Privacy Policy Language (CPPL) that enables mobile users to control who can access their context information, at what detail, and in which situation by specifying their context-aware privacy rules. Context-aware privacy rules map a set of privacy rules to one or more user’s situations, in which these rules are valid. Each time a user’s situation changes, a list of valid rules is updated, leaving only a subset of the specified rules to be evaluated by a privacy framework upon arrival of a context query. In the existing context-dependent privacy policy languages a user’s context is used as an additional condition parameter in a privacy rule, thus all the specified privacy rules have to be evaluated when a request to access a user’s context arrives. Keeping the number of rules that need to be evaluated small is important because evaluation of a large number of privacy rules can potentially increase the response time to a context query. CPPL also enables rules to be defined based on a user’s social relationship with a context requestor, which reduces the number of rules that need to be defined by a user and that consequently need to be evaluated by a privacy mechanism. This paper shows that when compared to the existing context-dependent privacy policy languages, this number of rules (that are encoded using CPPL) decreases with an increasing number of user-defined situations and requestors that are represented by a small number of social relationship groups.