The abilities of in-network caching in Named Data Networking (NDN) agrees to enable effective data dissemination throughout the globe without requiring the end-to-end communication infrastructure. ...The goal of this study is to develop an efficient content placement strategy for the NDN caching module to achieve the enhanced performance for the NDN network. In this work, optimizations of the problems in NDN-based caching strategies are formulated and to solve these problems, a new caching strategy is designed Named as Efficient Hybrid Content Placement (EHCP). The aim of the EHPC strategy is to reduce the multiple replications of homogeneous contents at numerous locations during data dissemination and to increase the diverse number of contents along the data delivery path. In addition, the effect of the cache-hit ratio, hop-decrement, and content redundancy is shown by comparing the proposed caching strategy with other state-of-the-art caching strategies in a simulation environment. The consequences show that the proposed strategy delivers an enhanced number of heterogeneous contents in terms of improving content diversity ratio along with the higher cache-hit ratio. Moreover, with different cache sizes, EHCP performs better in terms of content redundancy and hop-decrement as compared to the existing state-of-the-art strategies.
This paper presents a hybrid push/pull streaming scheme to take advantage of both the interval caching-based push method and the mesh-based pull method. When a new peer joins, a mesh-based pull ...method is adopted to avoid the overhead to reorganize the structure only if all of its potential preceding peers are likely to leave before the end of its playback. Otherwise, an interval caching-based push method is adopted so that the better performance of the push method can be maintained until it completes the playback. We demonstrate that our proposed scheme outperforms compared with when either the interval caching-based push method or mesh-based pull method is employed alone.
In recent years, Peer-to-Peer assisted Video-on-Demand (P2P VoD) has become an effective and efficient approach to distribute high-quality videos to large number of peers. In a P2P VoD system, each ...peer contributes storage to store several videos to help offload the server. The replication strategy, which determines the videos to be stored at each peer's local storage, plays an important role in system performance. There are two approaches: (a) solve a huge combinatorial optimization problem and (b) use simple cache replacement algorithms, such as Least-Frequently-Requested (LFR) or FIFO. The first approach needs to collect a large number of parameters whose values may be changing, and use some approximation method (such as linearization) to solve the optimization problem, both aspects have accuracy issues. In the second approach, a peer replaces some video in the cache with the currently viewed video, based on local information. While it is simple, we show their performance can be improved by a little centrally collected state information. Specifically, the needed feedback information is the current downloading rate provided by peers for each video. In this paper, we describe a hybrid replication strategy, and give detailed description of how the server collects and maintains the feedback information, and how peers use that information to determine what videos to store and indirectly control their uplink bandwidth contribution. This explains why the hybrid strategy is much simpler and more practical than the combinatory optimization approach. We then use simulation to demonstrate how our scheme out-performs the simple adaptive algorithms. Our simulation results also demonstrate how our scheme is able to quickly respond to peer churn and video popularity churn.
We propose a deterministic fluid model to understand the trade‐offs in the design of peer‐assisted video‐on‐demand (VoD) services. There are three entities in this model: peers (or end users), ...seeders (altruistic users that own one or several complete video items), and cache servers that store and forward videos with a limited capacity. Peers join the network, download one or multiple concurrent video streams (possibly different video items), and abort the system when they wish. Peers are assumed to cooperate in a BitTorrent‐based fashion, governed by tit‐for‐tat and fair availability. The issue is to minimize the expected downloading times, choosing the set of video items that should be stored in each cache server. We first prove that the deterministic model is globally stable, and find closed expressions for the expected waiting times. Then, we introduce a combinatorial optimization problem (COP), whose nature is similar to the multiknapsack problem (where the items are videos, and knapsacks are related with the storage of cache servers). The problem turns out to be NP‐complete. Therefore, we heuristically address the problem following a GRASP (greedy randomized adaptive search procedure) methodology. Finally, we simulate the new caching methodology with real‐life traces taken from YouTube logs. The results suggest that the peer‐to‐peer philosophy is both stable and cost‐effective for on‐demand streaming purposes.
In multimodal speech transcription of videos, audio and video are segmented using the concept of utterances, and transcription is performed independently on these segments. However, for real-world ...video transcription, multimodal information from past utterances can be leveraged to contextualize and improve video transcription. In this work, we first build multimodal speech recognition systems on instructional YouTube videos using the How2 corpus. We examine different visual representations and multimodal fusion techniques to infuse visual information into audio-only models. Then we explore methods to embed past context by training on longer input sequences. Experiments on the How2- 300h data demonstrate the importance of multi-modality and long-term context, which result in a 1 % WER absolute improvement over audio-only speech recognizers.
The exponential growth in the number of internet users for utilizing diversified applications, such as Voice over IP(VoIP), video on demand (VoD), video streaming and e-learning, has resulted in a ...steady increase in traffic on the transmission channel. Despite video streaming being a large contributor of traffic to both commercial and non-commercial uses, it still lags in the proper delivery of data to the users of cellular devices and another heterogeneous network. As the popularity of video streaming services increases, so does the user’s expectation for quality services. The poor quality of video streaming services has now become absolute in the video stream ecosystem. Therefore, video service providers have taken providing good Quality of Service to their users as their major goal. Provisioning of optimal QoS services in these applications become a challenging task for service providers in the presence of huge traffic and congestion. Since video streaming architecture follows a server-client architecture model, we propose to optimize the functions at the server-side to maximize QoS. The performance of the proposed architecture is compared with the existing architecture in terms of QoS features such as delay, delivery ratio and throughput for various data rates and buffer sizes. The proposed work is simulated using NS2 Simulator. The results show that, end to end delay is reduced by 10%, the delivery ratio is improved by 12% and throughput is improved by 17% when compared to the existing architecture.
Nowadays, video conferencing is one of the state-of-the-art and highly demanded technologies for conducting online communications. Providing real-time and rapid connection establishment is one of the ...chief principles of video conferences which is competently achieved by Multi-Protocol Label Switching (MPLS). Also, the emerging concept of Next Generation Networks (NGN) has accelerated the advancement of IP-based multimedia networks including Voice over IP (VoIP), Video on Demand (VoD) and IPTVs. MPLS networks have formed the foundation of fulfilling multimedia requirements in NGN. Therefore, Label Switching Path (LSP) routing is one of the highlighted challenges of Traffic Engineering (TE) in MPLS networks. MPLS routing algorithms attempt to increase the acceptance rate of requests and consequently meet the Quality of Service (QoS) constraints satisfaction. In this paper, we propose a novel routing algorithm that applies fuzzy rules to meet the bandwidth and end-to-end delay constraints in the routing. The proposed fuzzy mechanism constitutes a predicting system based on weighted fuzzy rules that filters the requests with higher resource demands. We name the proposed method as Fuzzy Bandwidth and Delay guaranteed Routing Algorithm (FBDRA). Schematically, FBDRA aims to defer the requests with maximum bandwidth and minimum end-to-end delays. All simulation experiments are carried out in MATLAB R2019a on different scenarios. We have gauged several metrics such as the number of accepted requests, average path length, energy consumption, load balancing, and average end-to-end delay to evaluate our proposed algorithm performance. The simulation results evidence the superiority of FBDRA in video conference applications.
Human-Computer Interaction (HCI) research has extensively employed eye-tracking technologies in a variety of fields. Meanwhile, the ongoing development of Internet Protocol TV (IPTV) has ...significantly enriched the TV customer experience, which is of great interest to researchers across academia and industry. A previous study was carried out at the BT Ireland Innovation Centre (BTIIC), where an eye tracker was employed to record user interactions with a Video-on-Demand (VoD) application, the BT Player. This paper is a complementary and subsequent study of the analysis of eye-tracking data in our previously published introductory paper. Here, we propose a method for integrating layout information from the BT Player with mining the process of customer eye movement on the screen, thereby generating HCI and Industry-relevant insights regarding user experience. We incorporate a popular Machine Learning model, a discrete-time Markov Chain (DTMC), into our methodology, as the eye tracker records each gaze movement at a particular frequency, which is a good example of discrete-time sequences. The Markov Model is found suitable for our study, and it helps to reveal characteristics of the gaze movement as well as the user interface (UI) design on the VoD application by interpreting transition matrices, first passage time, proposed ‘most likely trajectory’ and other Markov properties of the model. Additionally, the study has revealed numerous promising areas for future research. And the code involved in this study is open access on GitHub.
This work presents a comprehensive study, from an industrial perspective, of the process between the collection of raw data, and the generation of next-item recommendation, in the domain of ...Video-on-Demand (VoD). Most research papers focus their efforts on analyzing recommender systems on already-processed datasets, but they do not face the same challenges that occur naturally in industry, e.g., processing raw interactions logs to create datasets for testing. This paper describes the whole process between data collection and recommendation, including cleaning, processing, feature engineering, session inferring, and all the challenges that a dataset provided by an industrial player in the domain posed. Then, a comparison on the new dataset of several intent-based recommendation techniques in the next-item recommendation task follows, studying the impact of different factors like the session length, and the number of previous sessions available for a user. The results show that taking advantage of the sequential data available in the dataset benefits recommendation quality, since deep learning algorithms for session-aware recommendation are consistently the most accurate recommenders. Lastly, a summary of the different challenges in the VoD domain is proposed, discussing on the best algorithmic solutions found, and proposing future research directions to be conducted based on the results obtained.
In the coming era of telecommunication, the integration of satellite capabilities with emerging 5G technologies has been considered as a promising solution to achieve assured user experiences in ...bandwidth-hungry content applications. In this paper, we present our design for emerging Multi-access Edge Computing (MEC) based Video on Demand (VoD) services, which efficiently utilizes satellite and terrestrial integrated 5G network. Based on this framework, we propose and analyse the Video-segment Scheduling Network Function (VSNF), which is able to deliver enhanced quality of video consumption experiences to end-users. We specifically consider the layer video scenario, where it is possible to intelligently schedule layers of video segments via parallel satellite and terrestrial backhaul links in 5G. The key technical challenge is to optimally schedule the layered video segment over the two network link which are having distinct characteristics while attempting to enhance the Quality of Experience (QoE) for all the end-users in fair manner. We have conducted extensive set of experiments using real 5G testing framework in which gNB is integrated with core network using Geostationary Earth Orbit (GEO) satellite and terrestrial backhaul links. The results highlights the capability of our proposed content delivery framework for holistically delivering assured QoE, fairness among multiple video sessions, as well as optimised network resource efficiency.
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