Abstract-Personalized interactive television broadcasting requires real-time audiovisual processing at levels impractical in end-user equipment. However, guaranteeing Quality of Service (QoS) also remains a challenge for cloud-based solutions. This paper proposes a group QoS optimization approach to the problem that demonstrates significant improvements in the number of users being served. During the past decade, Interactive Television (ITV) based applications have gained a huge popularity within the consumer entertainment sector [1] . In order to facilitate the next generation of ITV, the ACTION-TV [2] project proposes a novel way of personalizing TV shows for a large number of concurrent social groups, in such a way that some individuals from each group can participate in the show while the rest enjoy the performance of their social peers. For example, in a quiz show, an interested user (i.e., active interactive-user) can become one of the contestants from the comfort of home and only his/her social group (i.e., passive interactive-users) can view his/her appearance. Consumer devices may not be able to support this type of interactive multimedia applications due to insufficient computational resources. Therefore, cloud support is a necessity for realizing these applications. Hence, an intercloud [3] based media processing architecture has been proposed for personalizing broadcast media [2] . Here, the users' activity data is acquired using consumer grade sensing devices such as the Kinect, and is uploaded to cloud-based media processing servers which personalize the broadcast media by re-authoring the show with the help of pre-prepared photorealistic 3D models. When the number of concurrent social groups proliferates, the cloud and communication infrastructure can be exhausted rapidly. Moreover, the communication delay between active interactive-users and the processing server must be maintained at an acceptable level to improve the user experience. Therefore, in the context of an application such as ACTION-TV, the design challenge is the selection of the cloud that hosts the processing server for each social group, such that overall users' QoS is maximized. Since users of a given social group may be connected to different clouds in a content delivery network (CDN) architecture as shown in Fig. 1 , the optimal location of the processing cloud must be determined based on the network conditions and data center availability. In addition, the processing requirements on the cloud infrastructures scale linearly with the number of social groups. Thus, optimum cloud resource allocation becomes even more challenging with the increasing numbers of users, social groups, clouds and network conditions. This paper proposes a group based QoS maximization approach to determine the processing cloud allocation for personalizing ITV content. In this section we introduce the ITV system, and describe the optimization problem and the associated constraints. Fig. 1 illustrates an example logical network diagram of an ITV system. For simplicity, it illustrates two user groups (i.e., social groups) connected to two Internet Service Providers (ISPs) and three potential computational resources (i.e., clouds). Users are categorized into "Actively Interacting (AI)" and "Passively Interacting (PI)" users, whose content are processed by a third "processing cloud" as indicated. The general problem for multiple user groups and cloud resources can therefore be expressed as follows. Let S denote the set of J clouds available in the network, U be the set of K ITV viewers belonging to N personalized social groups, T be the set of J×J virtual links between clouds, and E the set of K links between individual users and their ISPs. Thus,
In the context of a truly interactive ITV application, briefly described in the previous section REF , resource allocation must occur at both the application layer (e.g., processor and memory allocation) and the networking layer (e.g., network route selection).
Optimized resource distribution for interactive TV applications
2015 IEEE International Conference on Consumer Electronics (ICCE)
2015 IEEE International Conference on Consumer Electronics (ICCE)
Computer Science