Video streaming generates a substantial fraction of the traffic on the Internet. The demands of video streaming also increase the workload on the video server, which in turn leads to substantial ...slowdowns. In order to resolve the slowdown problem, and to provide a scalable and robust infrastructure to support on-demand streaming, helper-assisted video-on-demand (VoD) systems have been introduced. In this architecture, helper nodes, which are micro-servers with limited storage and bandwidth resources, download and store the user-requested videos from a central server to decrease the load on the central server. Multi-layer videos, in which a video is divided into different layers, can also be used to improve the scalability of the system. In this paper, we study the problem of utilizing the helper nodes to minimize the pressure on the central servers. We formulate the problem as a linear programming using joint inter- and intra-layer network coding. Our solution can also be implemented in a distributed manner. We show how our method can be extended to the case of wireless live streaming, in which a set of videos is broadcasted. Moreover, we extend the proposed method to the case of unreliable connections. We carefully study the convergence and the gain of our distributed approach.
•This study extends the expectation-confirmation model to the video-on-demand topic.•Satisfaction and perceived usefulness have an impact on the continuance intention.•Enjoyment has a strong impact ...on satisfaction.•Enjoyment did not have an impact on continuance intention.
Understanding how video-on-demand (VoD) technology evolves and what outcomes can be expected from the continued use of VoD has a greater impact on the long-term viability of an information system. This study examines the behavioural intentions of VoD consumers to continue using the service and further examines the influence of enjoyment on the intention to continue to use. To explore the usage continuance, we adopt the expectation confirmation model (ECM) for information technology and integrate it with the hedonic system adoption model. Our aim is to address the lack of literature around the ECM-IT model extended with hedonic variables and to contribute to the small but growing body of research of VoD services. The partial least squares (PLS) method was used to analyse an online survey of 205 individuals. The results suggest that satisfaction is the greatest predictor of the usage continuance intention and enjoyment strongly impacts satisfaction. Enjoyment in a hedonic system context is a manifestation of positive emotions that are translated into satisfaction. Our model explains 48.1% of the variance of the usage continuance and 53.8% of the satisfaction. The findings of this study offer an opportunity to better understand the long-term viability of VoD Services.
Caching at mobile edge servers can smooth temporal traffic variability and reduce the service load of base stations in mobile video delivery. However, the assignment of multiple video representations ...to distributed servers is still a challenging question in the context of adaptive streaming, since any two representations from different videos or even from the same video will compete for the limited caching storage. Therefore, it is important, yet challenging, to optimally select the cached representations for each edge server in order to effectively reduce the service load of base station while maintaining a high quality of experience (QoE) for users. To address this, we study a QoE-driven mobile edge caching placement optimization problem for dynamic adaptive video streaming that properly takes into account the different rate-distortion (R-D) characteristics of videos and the coordination among distributed edge servers. Then, by the optimal caching placement of representations for multiple videos, we maximize the aggregate average video distortion reduction of all users while minimizing the additional cost of representation downloading from the base station, subject not only to the storage capacity constraints of the edge servers, but also to the transmission and initial startup delay constraints of the users. We formulate the proposed optimization problem as an integer linear program to provide the performance upper bound, and as a submodular maximization problem with a set of knapsack constraints to develop a practically feasible cost benefit greedy algorithm. The proposed algorithm has polynomial computational complexity and a theoretical lower bound on its performance. Simulation results further show that the proposed algorithm is able to achieve a near-optimal performance with very low time complexity. Therefore, the proposed optimization framework reveals the caching performance upper bound for general adaptive video streaming systems, while the proposed algorithm provides some design guidelines for the edge servers to select the cached representations in practice based on both the video popularity and content information.
Multi-version video-on-demand (VoD) providers either store multiple versions of the same video or transcode video to multiple versions in real time to offer multiple-bitrate streaming services to ...heterogeneous clients. However, this could incur tremendous storage cost or transcoding computation cost. There have been some works regarding trading off between transcoding and storing whole videos, but they did not take into account video segmentation and internal popularity. As a result, they were not cost-efficient. This paper introduces video segmentation and proposes a segment-based storage and transcoding trade-off strategy for multi-version VoD systems in the cloud. First, we split each video into multiple segments depending on the video internal popularity. Second, we describe the transcoding relationships among versions using a transcoding weighted graph, which can be used to calculate the version-aware transcoding cost from one version to another. Third, we take the video segmentation, version-aware transcoding weighted graph, and video internal popularity into account to propose a storage and transcoding trade-off strategy, which stores multiple versions of popular segments and transcodes unpopular segments. We then formulate it as an optimization problem and present a heuristic divide-and-conquer algorithm to get an approximate optimal solution. Finally, we conduct extensive simulations to evaluate the solution; the results show that it can significantly lower the storage and transcoding cost of multi-version VoD systems.
As broadband networks using Fiber-to-the-x (FTTx) technologies are being increasingly deployed in access networks, video service, especially VoD (Video-on-Demand), is becoming more attractive to ...deploy. To provide efficient VoD service, a cost-effective service model is very important and a lot of research has been conducted over the past decade. This paper reviews the existing literature on this topic, focusing on the user behavior in VoD services and bandwidth-saving multicast streaming schemes, which are the most important aspects of VoD service. First, we review the user behavior in VoD such as video popularity, daily access pattern, and interactive VCR (Videocassette Recorder) properties from recent data. Each video title's rental frequency, i.e., video popularity, follows the Zipf distribution, and this popularity can change with time or by service provider's recommendation of videos. This overall request frequency for each video constitutes a specific pattern throughout the day and has a similar pattern every day. Second, we review the bandwidth-saving streaming schemes such as broadcasting, batching, patching, and merging, which use multicast streaming technologies and user buffer memory. We review the mechanism of each multicast streaming technology and compare their differences. We also review the recent trends on multicast streaming technologies, which is summarized as hybrid architecture which combines several multicast streaming technologies to obtain better performance. Next, we review how these multicast streaming technologies implement interactive VCR functions. We classify the VCR interactivity into discontinuous and continuous VCR actions and examine the principles for VCR support in multicast streaming schemes: caching some video data for discontinuous VCR support and allocating contingency channels for continuous VCR support. We review mechanisms of VCR support for different multicast streaming schemes. Through this survey, we provide an in-depth understanding of VoD service deployment.
As humans, we navigate a multimodal world, building a holistic understanding from all our senses. We introduce @MERLOT RESERVE, a model that represents videos jointly over time - through a new ...training objective that learns from audio, subtitles, and video frames. Given a video, we replace snippets of text and audio with a MASK token; the model learns by choosing the correct masked-out snippet. Our objective learns faster than alternatives, and performs well at scale: we pretrain on 20 million YouTube videos. Empirical results show that @MERLOT RESERVE learns strong multimodal representations. When finetuned, it sets state-of-the-art on Visual Commonsense Reasoning (VCR), TVQA, and Kinetics-600; outperforming prior work by 5%, 7%, and 1.5% respectively. Ablations show that these tasks benefit from audio pretraining - even VCR, a QA task centered around images (without sound). Moreover, our objective enables out-of-the-box prediction, revealing strong multimodal commonsense understanding. In a fully zero-shot setting, our model obtains competitive results on four video tasks, even outperforming supervised approaches on the recently proposed Situated Reasoning (STAR) benchmark. We analyze why audio enables better vision-language representations, suggesting significant opportunities for future research. We conclude by discussing ethical and societal implications of multimodal pretraining.
Increasing sales and profits requires dynamical allocation of advertising budgets as online advertising demand changes over time. Online platforms offer free and subscription advertising. The impact ...of subscription-based pricing and free platform services on supply chain (SC) profit and advertiser profit under uncertain demand has not been extensively studied. Moreover, there is no literature addressing how subscription platforms can benefit advertisers and the supply chain. To overcome this issue, we suggest a two-stage supply chain. Three situations are contrasted using game theory: (i) when both platforms give free service (Model F); (ii) where one platform offers subscription-based service while the other offers free service (Model S); and (iii) where the subscription-based platform and the advertiser are integrated (Model I). Compared to subscription-based advertising, free advertising is more profitable for advertiser. Users who move from free platforms to subscription-based platforms earn more profit. A case study illustrates our analytical findings.
We study a multi-access variant of the popular coded caching framework, which consists of a central server with a catalog of <inline-formula> <tex-math notation="LaTeX">N </tex-math></inline-formula> ...files, <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula> caches with limited memory <inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula> users such that each user has access to <inline-formula> <tex-math notation="LaTeX">L </tex-math></inline-formula> consecutive caches with a cyclic wrap-around and requests one file from the central server's catalog. The server assists in file delivery by transmitting a message of size <inline-formula> <tex-math notation="LaTeX">R </tex-math></inline-formula> over a shared error-free link and the goal is to characterize the optimal rate-memory trade-off. This setup was studied previously by Hachem et al. , where an achievable rate and an information-theoretic lower bound were derived. However, the multiplicative gap between them was shown to scale linearly with the access degree <inline-formula> <tex-math notation="LaTeX">L </tex-math></inline-formula> and thus order-optimality could not be established. A series of recent works have used a natural mapping of the coded caching problem to the well-known index coding problem to derive tighter characterizations of the optimal rate-memory trade-off under the additional assumption that the caches store uncoded content. We follow a similar strategy for the multi-access framework and provide new bounds for the optimal rate-memory trade-off <inline-formula> <tex-math notation="LaTeX">R^{*}(M) </tex-math></inline-formula> over all uncoded placement policies. In particular, we derive a new achievable rate for any <inline-formula> <tex-math notation="LaTeX">L \ge 1 </tex-math></inline-formula> and a new lower bound, which works for any uncoded placement policy and <inline-formula> <tex-math notation="LaTeX">L \ge K/2 </tex-math></inline-formula>. We then establish that the (multiplicative) gap between the new achievable rate and the lower bound is at most 2 independent of all parameters, thus establishing an order-optimal characterization of <inline-formula> <tex-math notation="LaTeX">R^{*}(M) </tex-math></inline-formula> for any <inline-formula> <tex-math notation="LaTeX">L\ge K/2 </tex-math></inline-formula>. This is a significant improvement over the previously known gap result, albeit under the restriction of uncoded placement policies. Finally, we also characterize <inline-formula> <tex-math notation="LaTeX">R^{*}(M) </tex-math></inline-formula> exactly for a few special cases.
Multicasting does not work well for video applications such as video on demand, where individual users request video content at discrete and unpredictable moments. In this paper, we study a ...concurrent video multicast orchestration solution for the caching-assisted and centrally controlled mobile networks. The proposed solution strives to obtain rapid video prefetch and effective traffic reduction. By rapid video prefetch, user devices can cache required contents in advance, which can effectively improve the streaming fluency in unsteady wireless environments, especially the highly mobile vehicular environment. We introduce a maximum-rate concurrent multicast (MRCM) problem whose solution is intended to maximize the total video delivery rate by a concurrent multicast manner, and prove that the MRCM problem is NP-hard. Then, we propose a dynamic concurrent multicast tree construction algorithm and a global concurrent multicast tree optimization algorithm to approximately solve the MRCM problem in polynomial time. We also introduce a cache competition mechanism that strives to maximize the total cache hit rate when the cache space is insufficient. Performance evaluations show that compared with the existing cache-assisted multicast delivery solutions, the proposed solution significantly reduces the video delivery duration and the traffic stress of backhaul links.
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