Transcoding in Online Rich-media Edutainment: a Dynamic Approach based on Content/Context Awareness

Transcription

1 Transcoding in Online Rich-media Edutainment: a Dynamic Approach based on Content/Context Awareness Silvia Mirri, Paola Salomoni, Stefano Ferretti Dipartimento di Scienze dell Informazione, Università di Bologna, Bologna, Italy {salomoni, mirri, sferrett}cs.unibo.it Abstract The use of adaptive systems provides means to ensure anytime, anywhere and any device entertainment experiences for heterogeneous players. In this paper, we present an approach to dynamically transcode edutainment contents and to facilitate user interactions in context-aware scenarios. We focus on those typologies of interactive online applications, for which edutainment activities are structured as a combination of delivery of rich media contents interleaved with interactive choices made by the users (e.g., multimedia quizzes). As a matter of facts, today s edutainment contents are mostly produced to be delivered through TV or broadband networks. Thus, typically they are not encoded to be used in nomadic contexts. It turns out that the use of an adaptation process (based on user profiles) results as an important tool to face with the poor computational and networking capabilities of (mobile) devices. Moreover, a higher level of accessibility can be guaranteed to users with disabilities, since contents are transcoded based on user preferences. Results gathered from a real evaluation assessment show the viability of our solution. Keywords- Transcoding Systems, Edutainment, Rich Media Content, Interfaces, Context-Aware Services. I. INTRODUCTION The research community is focusing on the need to devise new context-aware, ubiquitous services to support online edutainment. New smart solutions are required to enable users to enjoy services, regardless of their physical location, physical limitations, or of the device and network connection they use, etc. However, typical solutions currently in use do not seem to dynamically cope with the heterogeneity of profiles and preferences that users may have in a mobile, highly distributed context. The word edutainment is a neologism coined to combine the rather old idea of learning through playing with the new one of using modern technologies such as TV or computer. This term is thus now commonly used to describe a wide range of media which aims at educating while entertaining, or at entertaining while educating. In this context, we focus on a specific class of educational games, multimedia quizzes, which are traditionally distributed on CDs, DVDs, or through itv and Internet [11]. In particular, these games are based on the playout of videos and audio featuring a specific concept, combined with quizzes and simple games illustrating the topic. This class of entertainment applications, derived from documentaries and adapted to new interactive media, is widely used to explain real-world concepts, such as wildlife or, more generally, environmental and geographical topics [7]. Each game is structured as a graph of contents and quizzes. By interacting with the system, users (with right or wrong answers) design a path on the graph. Each path corresponds to a specific sequence of contents presented to users. This way, personalized forms of interactions emerge during each specific playing session. Mobility can effectively enhance opportunities of learning by augmenting the availability of edutainment applications and by allowing their use in outdoor learning. Furthermore, mobile interactive multimedia quizzes improve learning experiences from several points of view: Contents and quizzes can be used everywhere, following the user outdoor to places of study. For example, in an aquarium or at the zoo, a wide set of additional online information and games can be made locally available to enhance the user visit. The whole edutainment application can be contextualized to user preferences and needs, or simply adapted to the device in use. For example, differentiated contents might be delivered to adults or children. As a matter of facts, at the moment available edutainment contents are mostly created to be broadcasted on TV sets. Hence, they typically do not take into account constraints deriving from mobility (e.g., small interfaces, low-bandwidth connectivity). Furthermore, there is also the need to consider issues related to accessibility of contents to ensure anywhere, anytime, anyone, any device edutainment services. According to our previous considerations, we have devised a content management strategy based on two different criteria, content media type and user context. More specifically, multimedia contents are dynamically converted before their delivery. A specialized transcoding system, which acts as a proxy, is in charge of analyzing users profile and of dynamically scheduling a transcoding operation. Conversion processes of such contents into different media formats are achieved by exploiting facilities which are finally offered by (possibly distributed) Web Services. Our system only requires that contents are structured as SMIL [10] documents whenever /07/$ IEEE 1078

2 different media composing them must be synchronized. We carried out some evaluation tests to assess the performances of our approach. Results demonstrate the viability of our scheme. The remainder of this paper is organized as follows. Section II presents in detail our system architecture, by describing the transcoding module. Section III discusses on profiling issues at the basis of our approach. Section IV reports on results which have been gathered from the experimental evaluation we conducted to assess the efficacy of our scheme. Finally, Section V concludes the paper. II. SYSTEM ARCHITECTURE In this Section we introduce our system architecture and its functionalities for the support of edutainment quiz applications. Source contents are structured as graphs of rich media presentations, interleaved with interactive activities such as quizzes. We assume that synchronization among media composing a given content is performed by resorting to the SMIL technology [10], a well-known mark-up language which permits to describe temporal and spatial synchronization relationships among media contents. Needless to say, transcoding strategies might involve single media contents composing an edutainment multimedia quiz, as well as the synchronization specification of these contents, which needs to be modified accordingly. Thus, suitable methods are needed to manage SMIL documents and to transform these SMIL-based multimedia presentations into other formats (e.g., XHTML documents or video tracks). The system substantially performs two context-aware tasks: (i) it decides which contents have to be delivered to the user, on the basis of his/her profile and replies to quizzes; (ii) it transforms contents into a form which can be used by a specific user, on the basis of the user profile. Every user profile specifies different classes of information, which are stored and managed by the system in order to compute context-aware replies. Specifically, a user profile is characterized as follows [3, 8]: User preferences, needs, abilities. Device capabilities. Location-based information (if available). A simple sketch of our architecture is depicted on Figure 1. It shows the interactions between the two main components of the system: the Quiz Engine (QE) and the MultiMedia Transcoder (MMT). The client application (i.e., a multimedia player or a Web browser, depending on the context) communicates with the system sending the user profile and the first resource request. MMT stores the profile and, based on it, adapts the requested content and sends it back to the client application. The content consists in a sequence of rich media followed by an interactive quiz. By replying to the quiz, the user starts a new content request. MMT passes the reply to QE that evaluates it. Based on the user s answer and on the user s profile, QE replies with a new content, which is, finally, adapted by MMT and sent to the user. Summing up, every time a transcoding activity is required, MMT plans and performs an appropriate content adaptation. We now focus on the adaptation process. For the sake of clarity, we describe three typical scenarios of use. A B C Client Client Application Application Engine Engine MMT MMT MM MM Figure 1: System Architecture Let us first consider a user (user A in Figure 1), who accesses the application by using a fully equipped workstation connected to a broadband network. As a matter of facts, no transcoding activities are needed, since he is able to receive and perceive original contents and a SMIL player is installed on his computer. According to this situation, MMT will simply control the user profile, while QE will compute the next content to be delivered, based on user answers. Let us consider now user B who, compared to A, suffers a restriction of device capabilities. She is equipped with a smartphone and her profile details the characteristics of such a device. In this case, MMT adapts rich media contents, based on the limited capabilities of her smartphone, by reducing video dimension and by fitting all other contents to the display size. If the device is not provided with a SMIL player, MTT decomposes the rich media to obtain a more simple XHTML resource. Finally, let us consider user C which has a reduced ability to perceive contents. He is blind and his laptop is equipped with a screen reader. In this case, MMT adapts contents to be enjoyed sequentially to facilitate access through a screen reader, by converting the synchronized SMIL resource into a linear XHTML one. Instead of providing the user with a video file, alternative audio resources are exploited which fully describe what the video shows. Focusing on MMT, this software component is composed of the following modules (see also Figure 2) [5]: 1. The Multimedia Broker (MB), which manages requests for transcoding activities and sends transcoded contents back to the client. 2. The Profile Manager (PM), which controls a profile DB and stores users preferences. Once the Quiz Engine engages the multimedia transcoder, MB retrieves device and user profiles by querying PM. 3. The Transcoding Unit (TU) schedules the transcoding activities needed for an optimal content delivery to the user. Thus, as soon as a request arrives at MB, it interrogates PM and retrieves the user profile. Then, MB communicates such /07/$ IEEE 1079

3 Engine Client Client Browser Browser Multimedia Broker Transcoding Unit Unit MM MM Profile Manager Local WS MMT External WS information to TU, which defines a transcoding strategy, by matching user s profile with the specific rich multimedia resource. Afterwards, it computes a new version of the rich media by interacting with its known Web Services. Finally, it sends the adapted media content back to the client. Regarding the specific conversion steps, TU consists of a Web Service able to manage the SMIL specification and to schedule the needed content adaptation process. Moreover, TU resorts to two different sets of different Web Services. First, a cluster of Web Services is locally deployed near TU (i.e., on the same LAN), which are in charge of performing specific media types conversions. However, the system is also configured to exploit a second level of external Web Services deployed over the Internet. These are contacted only when TU identifies the need for a particular media conversion skill, which is not offered by the local Web Services. Finally, TU is supplied with a two-level caching system to improve system performances. Indeed, in order to avoid unnecessary re-computations of same media, both TU and local Web Services have separate caches, which maintain recently managed data and resources. Hence, the system takes advantage of caching mechanisms by transcoding data (for a specified format) once and by delivering them to users. III. PROFILING As described above, a profiling mechanism is required in order to describe client s devices as well as users preferences and needs. Several standards and solutions have been proposed in literature, such as Composite Capabilities/Preferences Profile (CC/PP) [9], User Agent Profile (UAProf) [4] and IMS Learner Information Profile (IMS LIP) [2]. Yet, a general and fully supported profiling proposal still misses. In fact, while CC/PP offers an open profiling mechanism, it defines a common vocabulary able to represents only technical characteristics of devices. Conversely, the IMS Global Learning Consortium has developed a profiling specification that strictly refers to accessibility issues, i.e., the so-called IMS Accessibility for Learner Information Package (ACCLIP) [1]. ACCLIP describes personal preferences (visual, aural, device) for tailored presentations of learning contents (e.g., preferred/required input devices or preferred content Figure 2: MultiMedia Transcoder (MMT) Architecture alternatives). However, ACCLIP enables to detail users in terms of accessibility needs, without considering the device characteristics. With this in view, our solution consists in exploiting both standards in order to consider both user s needs and device capabilities. By integrating the two approaches cited above, we obtain a complete profile fully describing the user and his device. This profile is sent to our online system during the first resource request. <accessforall schemaversion="1.0.29" xmlns=" xmlns:xsi=" xsi:schemalocation= " AccessForAllv1p0.xsd"> <context identifier="blind" xml:lang="it"> <display> <screenreader> <screenreadergeneric> <link value="speaklink"/> <link value="differentvoice"/> <speechrate value="500"/> <pitch value="0.8"/> <volume value="0.5"/> </screenreadergeneric> </screenreader> <braille> </braille> </display> <control>. </control> </context> </accessforall> Figure 3: Code Fragment from profile of Scenario C Basically, the main part of such user profile is devoted to describe device characteristics, fully defining the hardware and software equipments. In order to reduce the transmission of all the necessary data (which are standardized, once the device in use is known), we have integrated a profile data base containing user s preferences with a set of device capability descriptions derived from WURFL [12]. WURFL is a free and /07/$ IEEE 1080

4 open source project which offers information on devices. Currently, WURFL describes about different devices and identifies 300 characteristics. This updated, XML-based, open database of mobile devices is one of the most complete among those available on the Web. The use of WURFL allows a reduction of attributes specified by users since already existing profiles are exploited. For example, profiles that refer to scenarios A and B, mentioned in the previous Section, can be exhaustively described through the WURFL DB standard. However for scenario C, instead, an additional description is needed, based on the ACCLIP standard, which describes user accessibility requirements. Figure 3 shows a fragment of the profile code. In this example, the user declares a set of preferences about his used screen reader, (see element <screenreader> inside <display> element), as well as his Braille display characteristics (see <braille> element, partially omitted). All these elements are included inside the accessibility LIP element (<AccessForAll>), which drives the system transcoding process. Based on the obtained profile, the system produces a different version of each graphical and visual content. Figure 4: The Acquarium Fish Quiz on I-mate K-JAM IV. EXPERIMENTAL EVALUATION In this Section we describe how we conducted an experimental assessment to evaluate the performances of our system. First, we have referred to the three examples of Section 2, playing an interactive and multimedia (edutainment) quiz titled Aquarium fish quiz. A snapshot of the quiz running on I-mate K-JAM is depicted in Figure 4 above. Then, we performed a tests suite by using SOAtest Load Tester, an automated Web Service testing software, distributed by Parasoft [6], which allows to verify many aspects of a Web Service, such as WSDL validation, functional testing and performances. We considered different experimental scenarios in order to inspect advantages and disadvantages arising from the possible distribution of transcoding facilities over distributed Web Services. First, we analyzed a transcoding system that encloses all local transcoding facilities in a unique local TU subcomponent, without any need for resorting to satellites Web Services (in the following we will refer to this configuration as monolithic ). Then, we compared such a monolithic system with a distributed system configuration, according to which different transcoding services are deployed over different Web Services. During our tests, we resorted to different own-made Web Services as well as third-party Web Services, available on the Internet. Our developed Web Services (which are based on open source libraries) are devoted to manage and transform several image formats, to convert a wide set of different continuous media formats and to provide an appropriate transformations of SMIL documents. Experiments were conducted by transcoding 15 different resources (SMIL presentations, from 20 to 35 MB). Such parameters enabled us to assess the efficacy of our system on very different conditions. Three hosts have been exploited to distribute all the components of our architecture (apart from external Web Services). In particular, one node hosted our proxy, while the other two nodes were devoted to host our local Web Services. In order to generate user requests, we exploited two personal computers equipped with the above mentioned SOAtest program. During each trial, the maximum number of requests submitted to our system has been set to 500. For each simulated request, a random set of user profiles was created. Exploited profiles might differ in (i) screen dimensions settings (chosen from 484 entries, i.e., 22 possible screen widths and 22 possible screen heights), (ii) supported media formats (i.e., 7 different image formats, 3 video formats) and (iii) accessibility constraints (including the use of assistive technologies, such as Braille display or screen reader, and the inability for users to exploit some type of media, such as audio or video files). All simulations took place for one hour. All caches have been refreshed every 20 minutes. During our experiments we set the tester to submit requests for the delivery of edutainment contents, whose transcoding process involved both external and local Web Services to adapt all related resources. In particular, random requests were generated for SMIL-based resources. Media, which were included within these contents, were chosen among the already mentioned set of 15 resources. In this case, we observed that the completely distributed system performs better than the monolithic one, as pointed out in Table 1, Figures 5 and 6. Min Monolithic System Distributed System Max Average Completed Requests Table 1 - Transcoding Multimedia Presentations Times Table 1 reports results obtained when the number of requests is shaped as a typical bell curve, in order to assess the scalability of the system depending on the request number and so as to simulate a peak of requests. In that case, service responsiveness becomes a real issue that needs to be faced in order to avoid the Slashdot effect. Lower minimum, maximum and average times are obtained by using a completely distributed system. Moreover, we already mentioned that during our tests a high number of requests were submitted to a single proxy. Under such conditions, our tests /07/$ IEEE 1081

5 confirmed that our solution could be helpful also for services with a high number of actual users (min) Figure 5 - Average Execution Time (monolithic system) (min) Figure 6 - Average Execution Time (Distributed System) More in detail, Figure 5 and Figure 6 show the average response time observed during an hour trial by using, respectively, the monolithic system and the distributed system. The dark line reproduces (out of scale) the number of users which were connected at a given time, waiting for requested contents. As expected, a more regular behavior emerges from these Figures when resorting to a distributed approach. Indeed, the monolithic system presents a peak in response times. Such a high value underlines a bottleneck in the system. Practically speaking, when the number of queuing requests is over a threshold number, the monolithic system presents performance degradation. Such a result confirms that distributing transcoding facilities over different nodes may improve the general performances of the system. Putting in context these data on our three original scenarios, we can notice that in scenario A the transcoding system introduces a minimum delay of 78msec (109msec for the monolithic system). This additional delay is negligible, considering that the whole transmission requires more than 6sec for a resource of 20Mbyte. In scenario B, a different situation arises: the original video (800x600 dots) is reduced into a smaller one (320x240), which needs a reduced bandwidth. Specifically, the original 20Mbyte resource was reduced into an 8Mbyte-sized one. Notice that the sum of transmission time and transcoding time (on average 5sec) is much shorter than the simple transmission of the original video. Let us consider an UMTS connection: a performance up to 384 kbit/s is expected and, in the scenario B, it means user saves about 256sec when downloading the video. Moreover, the terminal does not support SMIL. Thus, an important improvement is achieved, i.e., the system delivers to the user an XHTML+MPEG version of the multimedia quiz which she can also enjoy on her mobile terminal. Finally, in scenario C the blind user obtains a more accessible version of the content. Accessibility is ensured by sending to the user a descriptive version of audio (alternative to the original one) and an XHTML+MPEG version of the whole multimedia quiz that can be easily interpreted by the screen reader. The additional delay introduced to accomplish this task is similar to that one considered in scenario A. V. CONCLUSIONS Content transcoding can be profitably exploited in multimedia edutainment where very heterogeneous users connect to the system. Context-aware edutainment services can be developed, which take into consideration client devices technical characteristics, user profiles and preferences. In this work, we have described a novel architecture, able to provide players with tailored and adapted rich multimedia game contents. Our transcoding system acts as a proxy able to dynamically schedule an adaptation activity, based on the client profile. We have carried out experiments that demonstrates the viability of our approach. REFERENCES [1] IMS Global Learning Consortium, IMS Learner Information Package Accessibility for LIP. Available from: [2] IMS Global Learning Consortium, IMS Learner Information Profile (LIP). Available from: [3] S. Mirri, D. Pantieri, M. Roccetti, P. Salomoni, A Multimedia Broker for Accessible Learning Objects Transcoding. In Proc. of the 2nd IEEE International Conference on Information and Communication Technologies: from Theory to Applications (ICTTA), Damascus (Syria), April [4] Open Mobile Alliance Profile Data (OMA), User Agent Profile (UAProf). Available from: [5] D. Pantieri, S. Mirri, P. Salomoni, RMob: Transcoding Rich Multimedia Contents through Web Services, in Proc. of the 2nd IEEE International Workshop on Networking Issues in Multimedia Entertainment - NIME 2006 (CCNC 2006 Satellite Workshop), Las Vegas (Nevada), January [6] Parasoft, SOA Test, [7] Public Broadcasting Service, New York: A Documentary Film Online. Available from: [8] P. Salomoni, S. Mirri, A Multimedia Broker for Ubiquitous and Accessible Rich Media Content Transcoding. In Proc. of 1st IEEE International Workshop on Networking Issues in Multimedia Entertainment - NIME (GLOBECOM 2004 Satellite Workshop), Dallas (Texas, USA) November [9] W3C Recommendation, Composite Capability/Preference Profiles (CC/PP): Structure and Vocabularies 1.0. Available from: , [10] W3C Recommendation, Synchronized Multimedia Integration Language 2.0. Available from [11] S. Walldén, A. Soronen, Edutainment. From Television and Computers to Digital Television. Available from: [12] WURFL, Wireless Universal Resource File Library. Available from: /07/$ IEEE 1082