Scheduling viewers effectively among different Content Delivery Network (CDN) providers is challenging owing to the extreme diversity in the crowdsourced live streaming (CLS) scenarios. Abundant algorithms have been proposed in recent years, which, however, suffer from a critical limitation: Due to their inaccurate feature engineering or naive rules, they cannot optimally schedule viewers. To address this concern, we put forward LTS (Learn to Schedule), a novel scheduling algorithm that can adapt to the dynamics from both viewer traffics and CDN performance. In detail, we first propose LTS-RL, an approach that schedules CLS viewers based on deep reinforcement learning (DRL). Since LTS-RL is trained in an end-to-end way, it can automatically learn scheduling algorithms without any pre-programmed models or assumptions about the environment dynamics. At the same time, to practically deploy LTS-RL, we then use the decision tree and imitation learning to convert LTS-RL into a more light-weighted and interpretable model, which is denoted as Fast-LTS. After the extensive evaluation of the real data from a leading CLS platform in China, we demonstrate that our proposed model (both LTS-RL and Fast-LTS) can improve the average quality of experience (QoE) over state-of-the-art approaches by 8.71--15.63%. At the same time, we also demonstrate that Fast-LTS can faithfully convert the complicated LTS-RL with slight performance degradation (< 2%), while significantly reducing the decision time (×7--10).

Today’s anywhere and anytime broadband connection and audio/video capture have boosted the deployment of crowdsourced livecast services (orcrowdcast). Bridging a massive amount of geo-distributed broadcasters and their fellow viewers, such representatives as Twitch.tv, Youtube Gaming, and Inke.tv, have greatly changed the generation and distribution landscape of streaming content. They also enable rich online interactions among the crowd, and strive to offer personalized Quality-of-Experience (QoE) for individual viewers. Given the ultra-large scale and the dynamics of the crowd, personalizing QoE however is much more challenging than in early generation streaming services. The rich interactions among the broadcasters, viewers, and the network system, on the other hand, also offer invaluable data that could be utilized towards informed management. This paper presentsDeepCast, an edge-assisted crowdcast framework that explores the sheer amount of viewing data towards intelligent decisions for personalized QoE demands. DeepCast seamlessly integrates cloud, CDN, and edge servers for crowdcast content distribution, and advocates a data-driven design that extracts the hidden information from the complex interactions among the system components. Through deep reinforcement learning (DRL), it automatically identifies the most suitable strategies for viewer assignment and transcoding at edges. We collect multiple real-world datasets and evaluate the performance of DeepCast with trace-driven experiments. The results demonstrate its flexibility and effectiveness towards better personalized QoE and lower cost for crowdcast systems.

Recently, the fusion of 360° video and multi-viewpoint video, called multi-viewpoint (MVP) 360° interactive video, has emerged and created much more immersive and interactive user experience, but calls for a low latency solution to request the high-definition contents. Such viewing-related features as head movement have been recently studied, but several key issues still need to be addressed. On the viewer side, it is not clear how to effectively integrate different types of viewing-related features. At the session level, questions such as how to optimize the video quality under dynamic networking conditions and how to build an end-to-end mapping between these features and the quality selection remain to be answered. The solutions to these questions are further complicated given the many practical challenges, e.g., incomplete feature extraction and inaccurate prediction.This paper presents an architecture, called iView, to address the aforementioned issues in an MVP 360° interactive video scenario. To fully understand the viewing-related features and provide a one-step solution, we advocate multimodal learning and deep reinforcement learning in the design. iView intelligently determines video quality and reduces the latency without pre-programmed models or assumptions. We have evaluated iView with multiple real-world video and network datasets. The results showed that our solution effectively utilizes the features of video frames, networking throughput, head movements, and viewpoint selections, achieving at least 27.2%, 15.4%, and 2.8% improvements on the three video datasets, respectively, compared with several state-of-the-art methods.

Recent years have seen booming development and great success in interactive crowdsourced livecast (i.e., crowdcast). Different from traditional livecast services, crowdcast is featured with tremendous video contents at the broadcaster side, highly diverse viewer side content watching environments/preferences as well as viewers' personalized quality of experience (QoE) demands (e.g., individual preferences for streaming delays, channel switching latencies and bitrates). This imposes unprecedented key challenges on how to flexibly and cost-effectively accommodate the heterogeneous and personalized QoE demands for the mass of viewers. In this paper, we propose DeepCast, an edge-assisted crowdcast framework, which makes intelligent decisions at edges based on the massive amount of real-time information from the network and viewers to accommodate personalized QoE with minimized system cost. Given the excessive computation complexity in this context, we propose a data-driven deep reinforcement learning (DRL) based solution that can automatically learn the best suitable strategies for viewer scheduling and transcoding selection. To our best knowledge, DeepCast is the first edge-assisted framework that applies the advance of DRL to explicitly accommodate personalized QoE optimization for crowdcast services. We collect multiple real-world datasets and evaluate the performance of DeepCast using trace-driven experiments. The results demonstrate the superiority of our DeepCast framework and its DRL-based solution.

Viewer scheduling among different CDN providers in crowdsourced live streaming (CLS) service is especially challenging due to the large-scale dynamic viewers as well as the time-variant performance of the content delivery network. A practical scheduling method should tackle the following challenges: 1) accurate modeling of viewer patterns and CDN performance; 2) intelligent workload offloading to save costs while guaranteeing the quality of service (QoS); 3) and ease of integration with practical CDN infrastructure in CLS platforms.

With the growing demand for crowdsourced live streaming (CLS), how to schedule the large-scale dynamic viewers effectively among different Content Delivery Network (CDN) providers has become one of the most significant challenges for CLS platforms. Although abundant algorithms have been proposed in recent years, they suffer from a critical limitation: due to their inaccurate feature engineering or naive rules, they cannot optimally schedule viewers. To address this concern, we propose LTS (Learn to schedule), a deep reinforcement learning (DRL) based scheduling approach that can dynamically adapt to the variation of both viewer traffics and CDN performance. After the extensive evaluation the real data from a leading CLS platform in China, we demonstrate that LTS improves the average quality of experience (QoE) over state-of-the-art approach by 8.71%-15.63%.

Crowdsourced live streaming (CLS), such as Twitch.tv and Inke.tv, has emerged as an important multimedia application in recent years. Video delivery in such CLS service involves two steps: 1) video uploading-video streaming (i.e., a live channel) generated from a broadcaster is uploaded to the server, which we call the “first mile” network and 2) video distribution-the video streaming is then delivered to viewers in the channel. Today's CLS services usually use conventional content delivery network solutions to address the video distribution problem, while little attention has been paid to improve the video uploading quality. Our measurement study shows that the first mile network causes 17% viewer rebuffers, and some viewers quit the channel once encountering rebuffer. In this paper, we propose a content harvest network (CHN) architecture to address the uploading problem in the CLS service. Specifically, the CHN architecture employs edge devices in the network as relays to receive the streaming uploaded by broadcasters and then forward to the central servers. On one hand, we need to reduce the latency since it is live streaming; on the other hand, we must provide sustainable upload bandwidth. It is challenging to achieve both at the same time, especially in such high dynamic system as CLS. In order to provide global optimal and real-time assignment, we propose a hybrid solution, i.e., centralized and distributed assignment. Specifically, we formulate the centralized relay assignment problem as an optimization problem to achieve both low latency and sustainable bandwidth. To cope with the frequent channel establishments, we use a multi-armed bandit method to characterize the time-variant network condition. Experiment results on a large-scale trace provided by Inke.tv show that our solution can reduce the overall viewer cost by 40% compared to state-of-the-art solutions. The viewers' rebuffer can also be reduced by 50%.

Adaptive bitrate streaming enables video users toadapttheir playing bitrates to the real-time network conditions, hence achieving the desirable quality-of-experience (QoE). In a multi-user wireless scenario, however, existing single-user based bitrate adaptation methods may fail to provide the desirable QoE, due to lack of consideration of multi-user interactions (such as the multi-user interferences and network congestion). In this work, we propose a novel user cooperation framework based onuser-provided networkingfor multi-user mobile video streaming over wireless cellular networks. The framework enables nearby mobile video users to crowdsource their cellular links and resources for cooperative video streaming. We first analyze the social welfare performance bound of the proposed cooperative streaming system by introducing a virtual time-slotted system. Then, we design a low complexity Lyapunov-based online algorithm, which can be implemented in an online and distributed manner without the complete future and global network information. Numerical results show that the proposed online algorithm achieves an average 97 percent of the theoretical maximum social welfare. We further conduct experiments with real data traces, to compare our proposed online algorithm with the existing online algorithms in the literature. Experiment results show that our algorithm outperforms the existing algorithms in terms of both the achievable bitrate (with an average gain of 20$\sim$30 percent) and social welfare (with an average gain of 10$\sim$50 percent).

Edge caching is a key technology to accelerate the content delivery and to mitigate the network congestion. As a real-world example, Microsoft has been providing the edge caching service with a usage-based pricing mechanism since 2009. However, the challenge of designing a better pricing mechanism lies in balancing the finer grain size of the mechanism and the complexity of business operations it required. Our work proposes a novel location dependent pricing mechanism for the edge caching market which both improves the Internet service provider's (ISP) revenue and the social welfare. First, we introduce the location dependent pricing mechanism (LDP) that offers location-specific price, considering the popularity differences between the edge nodes(e.g. base stations, WiFi access points) at different locations. We capture the interactions between the content providers (CPs) and the ISP as a two-stage Stackelberg game and derive the CP's best responses and the ISP's optimal pricing strategy. Then we provide a partial location dependent pricing scheme (PLDP) which limits the number of price tiers to provide a balance between the optimality and economic viability. Finally, through extensive numerical simulation, we find that the LDP (or PLDP) improves the revenue of the ISP by 31%↑50% (23%↑%35) and also improves the social welfare by 46% (%32) on average, compared to the uniform pricing scheme.

Many efforts have been made on sensing ultrasound with commercial-off-the-shelf (COTS) mobile devices in the recent literature. Yet due to the limited sound sample rate, current COTS mobile devices can not directly capture any sound at the frequency over 24 kHz. This issue prevents true ultrasound, of which the frequency is typically over 40 kHz, from benefiting the existing sound sensing applications. In this work, we show that by subtly customizing the sampling process, we can make COTS mobile devices hear the true ultrasound that is typically beyond their capability to fully capture. Particularly, we present a system that enable COTS mobile devices to sense the power spectrum density (PSD) of true ultrasounds, of which the frequency can be as high as 60 kHz.

The emerging 5G mobile networking promises ultrahigh network bandwidth and ultra-low communication latency (100ms), due to its store-and-forward design and the physical barrier from signal propagation speed, not to mention congestion that frequently happens. Caching is known to be effective to bridge the speed gap, which has become a critical component in the 5G deployment as well. Besides storage, 5G base stations (BSs) will also be powered with strong computing modules, offering mobile edge computing (MEC) capability. This paper explores the potentials of edge computing towards improving the cache performance, and we envision a learning-based framework that facilitates smart caching beyond simple frequency- and time-based replace strategies and cooperation among base stations. Within this framework, we develop DeepCache, a deep-learning-based solution to understand the request patterns in individual base stations and accordingly make intelligent cache decisions. Using mobile video, one of the most popular applications with high traffic demand, as a case, we further develop a cooperation strategy for nearby base stations to collectively serve user requests. Experimental results on real-world dataset show that using the collaborative DeepCache algorithm, the overall transmission delay is reduced by 14%~22%, with a backhaul data traffic saving of 15%~23%.

With rapid development of radio frequency identification (RFID) technology, ever-increasing research effort has been dedicated to devising various RFID-enabled services. The key tag monitoring, which is to detect anomaly of key tags, is one of the most important services in such important Internet-of-Things applications as inventory management. Yet prior work assumes that all tags are armed with hashing functionality and a reader would report channel states in every slot, which is not supported by commercial off-the-shelf (COTS) RFID tags and readers. To bridge this gap, this paper is devoted to enabling key tag monitoring service with COTS devices. In particular, we introduce two anomaly monitoring protocols to detect whether there is any key tag absent from the system. The first protocol employs Q-query that works in an analog frame slotted Aloha paradigm to interrogate tags and collect tag IDs. An anomaly event will be found if at least one key tag ID is not present in the collected ones. To reduce time cost of the first protocol resulted from tag collisions, we present a collision-free method that uses select-query to specify a key tag to reply in each slot. Once there is no response in a slot, the specified key tag is regarded as a missing tag. We conduct experiments to evaluate two protocols.

Recent years have witnessed an explosion of crowdsourced livecast (i.e., live broadcast) services, in which any Internet users can act as broadcasters to publish livecasts to fellow viewers. To help grow broadcasters' channels, crowdsourced livecast services provide a past-broadcast saving service, allowing viewers to watch the replays they may have missed. Our real-trace measurement and questionnaire survey show that (1) the duration of most of livecasts is extremely long; (2) a much longer duration largely affects the viewers' Quality-of-Experiences (QoE) when watching the replays. To address this issue and improve viewers' QoE, we propose a crowdsourced framework HighCast based on the interactive messages contributed by the viewers in crowdsourced livecast services. According to a highlight-aware detection module, HighCast can exploit the detection results to schedule the content placement by considering the importance of the predicted streaming highlights. The trace-based evaluations illustrate that the proposed framework improves the prediction accuracy and reduces the viewing latency.

Enabling users to interact with broadcasters and audience, the crowd-interactive livecast greatly improves viewer's quality of experience (QoE) and attracts millions of daily active users recently. In addition to striking the balance between resource utilization and viewers' QoE met in the traditional video streaming service, this novel service needs to take supererogatory efforts to improve the interaction QoE, which reflects the viewer interaction experience. To tackle this issue, we conduct measurement studies over a large-scale dataset crawled from a representative livecast service provider. We observe that the individual's interaction pattern is quite heterogeneous: only 10% viewers proactively participate in the interaction, and the rest viewers usually watch passively. Incorporating the insight into the emerging cloud-edge architecture, we propose a framework PIECE, which optimizes the Personalized Interaction Experience with Cloud-Edge architecture (PIECE) for intelligent user access control and livecast distribution. In particular, we first devise a novel deep neural network based algorithm to predict users' interaction intensity using the historical viewer pattern. We then design an algorithm to maximize the individual's QoE, by strategically matching viewer sessions and transcoding-delivery paths over cloud-edge infrastructure. Finally, we use trace-driven experiments to verify the effectiveness of PIECE. Our results show that our prediction algorithm outperforms the state-of-the-art algorithms with a much smaller mean absolute error (40% reduction). Furthermore, in comparison with the cloud-based video delivery strategy, the proposed framework can simultaneously improve the average viewers QoE (26% improvement) and interaction QoE (21% improvement), while maintaining a high streaming bitrate.

Cellular data sponsoring (CDS) is a traditional data sponsor scheme widely used in cellular video delivery networks, where content providers (CPs) bear the cellular data downloading cost for mobile video users (MUs), so as to attract more MUs and achieve higher revenue (e.g., via more attached advertisements). Edge caching sponsoring (ECS) is a novel data sponsor scheme recently introduced in the emerging 5G network, where CPs cache popular video contents on the edge network in advance and deliver them to local MUs directly. Thus, it can not only achieve the benefits of CDS (i.e., attracting more MUs and achieving higher revenue), but also reduce the congestion of backhaul network. In this work, we will perform a competitive analysis of CDS and ECS for mobile video streaming. Specifically, we consider a mobile video delivery network with two CPs who adopt CDS and ECS, respectively. MUs can choose one or neither of these two sponsor schemes (from the corresponding CPs) for his video content requests. We formulate the interaction of CPs and MUs as a two-stage Stackelberg game, where CPs act as leaders determining the efforts of their adopted sponsor schemes in the first stage, and MUs act as followers choosing the best sponsor schemes for their content requests in the second stage. We analyze the sub-game perfect equilibrium systematically for both cooperative and competitive scenarios (depending on whether two CPs cooperate or compete with each other). Numerical results show that in the competitive scenario, the joint sponsor of ECS and CDS can increase the total MU payoff by 36% ~ 140%, comparing with that with only one sponsor scheme. Moreover, the CPs can benefit more from ECS than from CDS when the revenue is higher.

The explosive growth of mobile video traffic introduces new challenges for the network infrastructure. Edge caching, as one of the key technologies in 5G wireless networks, has shown great potential to improve the quality of mobile video services by reducing the transmission overhead over backhaul links. With edge caching, content providers (CPs) need to decide not only the traditional data sponsoring strategy on cellular networks (where CPs cover part or all of the mobile users' cellular data cost), but also a novel cache sponsoring strategy on the edge caching networks (where CPs place part of contents on edge networks in advance). In this paper, we study the joint optimization of both sponsors on cellular and edge caching networks for a single CP, aiming at maximizing the CP's revenue. Specifically, we formulate the joint optimization problem as a two-stage sequential decision problem. In stage I, the CP determines the edge caching policy (for a relatively long time period). In stage II, the CP decides the real-time data sponsoring strategy for each content request within the period. We analyze this two-stage decision problem systematically. First, we propose an online sponsoring strategy in stage II based on Lyapunov optimization framework. Then, we propose an edge caching strategy in stage I via predicting the number of aggregate user requests. Simulations on real data traces show that such a joint optimization policy can increase the CP's revenue by 124%-154%, comparing with the traditional data sponsoring policy (i.e., without edge caching). Moreover, the proposed online strategy can achieve 90% of the maximum revenue in the offline benchmark.

Mobile personal livecast (MPL) services are emerging and have received great attention recently. In MPL, numerous and geo-distributed ordinary people broadcast their video contents to worldwide viewers. Different from conventional social networking services like Twitter and Facebook, which have a tolerance for interaction delay, the interactions (e.g., chat messages) in a personal livecast must be in real-time with low feedback latency. These unique characteristics inspire us to: (1) investigate how the relationships (e.g., social links and geo-locations) between viewers and broadcasters influence the user behaviors, which has yet to be explored in depth; and (2) explore insights to benefit the improvement of system performance. In this article, we carry out extensive measurements of a representative MPL system, with a large-scale dataset containing 11M users. In the current costly and limited cloud-based MPL system, which is faced with scalability problem, we find: (1) the long content uploading distances between broadcasters and cloud ingesting servers result in an impaired system QoS, including a high broadcast latency and a frequently buffering events; and (2) most of the broadcasters in MPL are geographically locally popular (the majority of the views come from the same region of the broadcaster), which consume vast computation and bandwidth resources of the clouds and Content Delivery Networks. Fortunately, the emergence of edge computing, which provides cloud-computing capabilities at the edge of the mobile network, naturally sheds new light on the MPL system; i.e., localized ingesting, transcoding, and delivering locally popular live content is possible. Based on these critical observations, we propose an edge-assisted MPL system that collaboratively utilizes the core-cloud and abundant edge computing resources to improve the system efficiency and scalability. In our framework, we consider a dynamic broadcaster assignment to minimize the broadcast latency while keeping the resource lease cost low. We formulate the broadcaster scheduling as a stable matching with migration problem to solve it effectively. Compared with the current pure cloud-based system, our edge-assisted delivery approach reduces the broadcast latency by about 35%.

Data sponsoring is a widely-used incentive method in today's cellular networks, where video content providers (CPs) cover part or all of the cellular data cost for mobile users so as to attract more video users and increase data traffic. In the forthcoming 5G cellular networks, edge caching is emerging as a promising technique to deliver videos with lower cost and higher quality. The key idea is to cache video contents on edge networks (e.g., femtocells and WiFi access points) in advance and deliver the cached contents to local video users directly (without involving cellular data cost for users). In this work, we aim to study how the edge caching will affect the CP's data sponsoring strategy as well as the users' behaviors and the data market. Specifically, we consider a single CP who offers both the edge caching service and the data sponsoring service to a set of heterogeneous mobile video users (with different mobility and video request patterns). We formulate the interactions of the CP and the users as a two-stage Stackelberg game, where the CP (leader) determines the budgets (efforts) for both services in Stage I, and the users (followers) decide whether and which service(s) they would like to subscribe to. We analyze the sub-game perfect equilibrium (SPE) of the proposed game systematically. Our analysis and experimental results show that by introducing the edge caching, the CP can increase his revenue by 105%.

Edge caching technique is becoming more and more important in 5G network architecture, which shows great performance in terms of mitigating the backbone network congestion and reducing the delivery latency. The key idea of this technique is to cache popular contents in the small base stations(SBSs) which locate at the edge of the wireless network, and deliver these contents to nearby mobile users(MUs) from the cache directly. The collaborative in-cluster prefetching is improving the network performance even more. In this paper, we study the case where one monopolistic state-owned mobile network operator (MNO) offers edge caching service to multiple Content Providers(CPs), and focus on the mechanism design for the cache space allocation in this market. As the caching policy on each edge node is coupled with each other due to in-cluster collaboration, one time auction or flat pricing will both harm the social welfare. We propose a 2-optimal sequential auction mechanism (SAM) to solve this problem, which improves the social welfare by 20%~70% compared to the traditional Vickery-Clarke-Groves(VCG) mechanism.

Mobile online social network services have seen rapid expansion; thus, the corresponding huge amounts of user-generated social media contents propagating between users via social connections have significantly challenged the traditional content delivery paradigm. First, replicating all the contents generated by users to edge servers that well “fit” the receivers becomes difficult due to limited bandwidth and storage capacities. Motivated by device-to-device (D2D) communication, which allows users with smart devices to transfer content directly, we propose replicating bandwidth-intensive social contents in a device-to-device manner. Based on large-scale measurement studies on social content propagation and user mobility patterns in edge-network regions, we observe the following: (1) Device-to-device replication can significantly help users download social contents from neighboring peers. (2) Both social propagation and mobility patterns affect how contents should be replicated. (3) The replication strategies depend on regional characteristics (e.g., how users move across regions). Using these measurement insights, we propose a propagationand mobility-aware content replication strategy for edge-network regions, in which social contents are assigned to users in edge-network regions according to a joint consideration of social graphs, content propagation, and user mobility. We formulate the replication scheduling as an optimization problem and design a distributed algorithm using only historical, local, and partial information to solve it. Trace-driven experiments further verify the superiority of our proposal: compared with conventional pure-movement-based and popularity-based approaches, our design can significantly improve (2 - 4-fold improvement) the amount of social content successfully delivered via device-to-device replication.

In this work, we study the joint optimization of edge caching and data sponsoring for a video content provider (CP), aiming at reducing the content delivery cost and increasing the CP's revenue. Specifically, we formulate the joint optimization problem as a two-stage decision problem for the CP. In Stage I, the CP determines the edge caching policy (for a relatively long time period). In Stage II, the CP decides the real-time data sponsoring strategy for each content request within the period. We first propose a Lyapunov- based online sponsoring strategy in Stage II, which reaches 90% of the offline maximum performance (benchmark). We then solve the edge caching problem in Stage I based on the online sponsoring strategy proposed in Stage II, and show that the optimal caching policy depends on the aggregate user request for each content in each location. Simulations show that such a joint optimization can increase the CP's revenue by 30%↑100%, comparing with the purely data sponsoring (i.e., without edge caching).

Accurate mobility prediction is becoming increasingly important in human behavior research, mainly due to many location-based applications such as mobile social networks and mobile advertisements. In this work, we propose a new crowd-sourced human mobility prediction model for public regions. We first analyze human trajectories collected through a cluster of densely deployed Wi-Fi access points (AP) in a shopping mall, and then characterize the close relationship between the human mobility patterns and the spatio-temporal contexts. Based on the distinct features of human trajectories in different types of public regions, we further propose a Markov-based crowdsourced mobility prediction method utilizing spatio-temporal contexts. We evaluate the performance of the proposed method using real traces, and show that our method is 28% more accurate in predicting human location transitions and incurs 14% smaller error in stay time prediction than the baseline methods.

Adaptive bitrate video streaming is a widely-used technology for mobile video streaming over HTTP. In this work, we study a crowdsourced video streaming framework, which enables nearby mobile users to crowdsource their radio resources for cooperatively adaptive bitrate video streaming. We propose a multi-dimensional auction based incentive mechanism to promote the user cooperation, supporting the asynchronous downloading and the bitrate adapting of video users. In this mechanism, each user initiates an auction whenever he is ready to download a new data segment in an asynchronous fashion, and all nearby users compete for the downloading opportunity by submitting a multidimensional bid consisting of the intended segment bitrate and the associated value. Design of such a multi-dimensional auction is very challenging, as we need to guarantee the user's truthful reporting on the information on multiple dependent dimensions. We first propose a truthful second-score (multi-dimensional) auction framework, within which we further derive the efficient mechanism that maximizes the social welfare (of each segment downloading) and the sub-optimal mechanism that approximately maximizes the auctioneer payoff. Experiment results show that our proposed crowdsourced streaming can achieve 60% ˜ 76% of the maximum social welfare even when 80 percentage of users lose their direct network connections.