To contribute to publishing pedestrian networks as open data, this paper introduces a curb height estimation method by using 2DLiDAR mounted on e-Bike. We develop a prototype system by using e-Bike, 2DLiDAR and other sensors, and collect actual curb data around Waseda University. From accuracy evaluations, the results confirm that the prototype can estimate road level differences and positions with high accuracy.

In video streaming on the internet, effective encoding recipes (i.e. bitrate-resolution pairs) are a main obstacle to deliver high-quality video streams. We developed a method to generate an encoding recipe that considers subjective visual quality with one just-noticeable difference (JND) distance. However, this method requires excessive computation time, which is not directly applicable for live streaming. In this paper, in order to provide a light-weight method for live streaming, we developed three acceleration techniques: resolution extrapolation, VMAF skipping and sampled objective measure calculation. These techniques are heuristic, but greatly contribute to reducing computational cost. Experimental results demonstrate that the proposed method achieves a significant reduction in computation time without significant effects on rate-JND characteristics.

Dynamic adaptive video streaming over HTTP (DASH) is widely studied and has been adopted in modern video players to ensure user quality of experience (QoE). In DASH, adaptive bitrate control is a key part whose ultimate goal is to maximize video bitrate while minimizing rebuffering. Throughput prediction plays an important role in helping select the proper video bitrate dynamically. In this paper, we studied the influence of throughput prediction on adaptive video streaming. Because the real-world network is dynamic, different methods need to be tested with large-scale deployments and analyzed statistically. However, this is difficult in academic research. Therefore, we established a reproducible trace-based emulation environment, which enables us to compare different methods quantitatively under the artificially same condition, with limited experiments. The throughput prediction methods are implemented into DASH to evaluate the effect on QoE for video streaming. The results indicate that the prediction method using long short-term memory (LSTM) performs better than the other methods. However, throughput prediction alone is not enough to ensure high QoE. To further improve the QoE, we proposed the decision map method (DMM), where the buffer occupancy is also incorporated to make a selection. By using this decision map, the choice of bitrate can be smarter than that when only prediction information is used. The total QoE is further improved by 32.1% in the ferry trace, which shows the effectiveness of DMM in further improving the performance of throughput prediction in adaptive bitrate control.

This paper introduces recent trends in video streaming and four methods proposed by the authors for video streaming. Video traffic dominates the Internet as seen in current trends, and new visual contents such as UHD and 360-degree movies are being delivered. MPEG-DASH has become popular for adaptive video streaming, and machine learning techniques are being introduced in several parts of video streaming. Along with these research trends, the authors also tried four methods: route navigation, throughput prediction, image quality assessment, and perceptual video streaming. These methods contribute to improving QoS/QoE performance and reducing power consumption and storage size.

To ensure good quality of experience for user when transmitting video content, throughput prediction can contribute to the selection of proper bitrate. In this paper, we propose a throughput prediction method with recurrent neural network (RNN) model. Experiments are conducted to evaluate the methods, and the results indicate that proposed method can decrease the prediction error by a maximum of 29.39% compared with traditional methods.

Named Data Networking (NDN) has been recently introduced as a new network architecture. The implementation of adaptive bit-rate video streaming on NDN is one of the most challenging tasks. The in-network caching feature of NDN could mislead a consumer to overestimate the end-to-end throughput for the next video segment. In this paper, we propose a strategy for more efficient rate adaptation by allowing Interest message adaptation in a hop-by-hop fashion. Moreover, the proactive content caching is designed to be utilized on the related router when a bottleneck on the transmission path was detected. The experiments using NDN-JS and DASH-JS demonstrate that our proposed solution provides better average stream bit-rate than the traditional rate adaptation method. Also, the link bandwidth consumption for video data transmission using the proposed scheme could be reduced efficiently.

Throughput prediction is essential for ensuring high quality of service for video streaming transmissions. However, current methods are incapable of accurately predicting throughput in mobile networks, especially for moving user scenarios. Therefore, we propose a TCP throughput prediction method TRUST using machine learning for mobile networks. TRUST has two stages: user movement pattern identification and throughput prediction. In the prediction stage, the long short-term memory (LSTM) model is employed for TCP throughput prediction. TRUST takes all the communication quality factors, sensor data and scenario information into consideration. Field experiments are conducted to evaluate TRUST in various scenarios. The results indicate that TRUST can predict future throughput with higher accuracy than the conventional methods, which decreases the throughput prediction error by maximum 44% under the moving bus scenario.

Recently, to provide a low-latency mobile computing platform, Mobile Edge Computing (MEC) is proposed. In this paper, we first summarized the feature capabilities of MEC, such as content distribution and caching, computational offloading and multimedia Internet of Things (IoT). Then, to understand recent research efforts of multimedia MEC, we briefly highlight the research efforts in terms of above three capabilities: how to achieve edge caching in video distribution, how to schedule computational offloading to the cloud and how the communication quality degradation affects to the user experience of multimedia IoT. Finally, we addressed the emerging research issues of multimedia MEC to improve reliability and robustness of multimedia MEC.

Disaster detection is an important research topic and draws great attentions from both industry and academia. In this paper, we study the hybrid landslide detection system, which utilizes the video surveillance camera and multiple kinds of sensors and can detect the landslide automatically. Edge processing is adopted in this hybrid system to fuse the sensor data based on the Dempster-Shafer (D-S) theory, i.e. utilizing multiple sensors' information to calculate the possibility of the landslide. Edge processing can make faster decisions than the control center, the results of the edge processing are then used to schedule the sensor's transmission frequency and video transmission under the network constraints in this system. The simulation results show that the proposed scheme outperforms the competing schemes in typical network scenarios.

Throughput prediction contributes a lot to adaptive bitrate control, adjusting the quality of video streaming accordingly to offer smooth media transmission and save energy at the same time. To solve the problem of throughput prediction for real time communication, this paper puts forward a new history-based throughput prediction method applying Hidden Markov Model in mobile networks. The main purpose of this method is to predict future throughput for real time communication in mobile network. Our novel approach utilizes Hidden Markov Model (HMM) with Gaussian Mixture Model (GMM) to deal with history time series of throughput and judge fluctuation factor with total variance when predicting future throughput. By conducting experiments with the new methodology, we compare the accuracy of the proposed method with three other conventional prediction methods. Results show our proposed method could identify data fluctuation effectively and predict future 100s throughput with high accuracy in various situations.

In order to analyze system latency of edge computing for multimedia data processing, we propose a model of system latency of two different distributed processing scenarios: pipeline and parallel processing. We validate the model by using actual cloud services and human detection processing.

In this study, we propose a broadcasting and communication integrated platform that can retransmit 8K broadcasting contents from a TV receiving set to mobile devices. We then evaluate the QoS characteristics and the qualities of 8K contents in this platform. Evaluation results conclude that 8K broadcasting contents can be retransmitted to mobile devices smoothly and their qualities are nearly as high as those of assumed 8K broadcasting service in Japan.

Providing robust video streaming along with efficient wireless resource usage is necessary for mobile users, especially on subway, and mobile carriers. To achieve this, we propose a highly-reliable buffer strategy based on long-term throughput prediction. Our approach has two elements which are called "long-term throughput prediction" and "guaranteed playout buffer filling mechanism." To avoid any video freeze due to network quality degradation, our approach calculates the optimal amount of playout buffer and schedules video download timing in a theoretical manner. We evaluate its performance via experiments in real environment. Evaluations conclude that our approach can provide highly-reliable video streaming and also achieve to reduce the average playout buffer size on the client.

Proactive content caching scheme utilizing transportation systems, especially on trains, was proposed in order to provide a robust content delivery with efficient wireless resource usage. This system requires content servers with NDN capability to be placed on every station and trains. The mechanism is to pre-cache the contents that users request to the station server before the train arrives, and the train server caches the content during its stoppage time. With this mechanism, users are able to have a continuous playback of videos while riding on trains. In this paper, we have proposed a browser-based implementation, called DASH-NDN-JS, for this proactive content caching scheme. We evaluate this scheme, and experiment with multiple users to see how it will affect the video quality each user will achieve and the bandwidth consumption between the connections. Our evaluations conclude that the increase of users lowers the video quality, but avoids congestion depending on what video content each user will want to request.

Effective use of various wireless network interfaces is expected. In this paper, we evaluate Quality of Service characteristics of mobile 4K video using a radio communication quality map. We collect radio communication quality (e.g. throughput) by smartphones and visualize the quality in a heat map. Using this map, we select two routes that are expected to bring the highest and the lowest communication quality. We then evaluate QoS characteristic of 4K video streaming using MPEG-DASH on these two routes. The result shows that a good route user can achieve 1.5 times higher communication quality than a bad route user.

In this paper, we evaluate performances of 4K/2K video streaming using DASH-JS over 2.4/5GHz WiFi networks. We confirmed that fluctuation of reception quality was mainly caused by the minimum representation rate of MPEG-DASH contents and overlapped ON cycles for packet transmission.

In wireless networks, it is important to realize energy-efficient video delivery. To do this, we introduce energy-efficient video streaming over named data networking (NDN). In our proposed approach, we focus on two areas, namely, to improve the throughput performance by Interest aggregation, and to reduce the overhead energy using playout buffer-size control. We evaluate the power savings realized by our method using a hardware power-measurement tool. Our results show that Interest aggregation can realize an approximately 50% reduction in energy compared to conventional NDN implementation, and a large playout buffer size can reduce the energy by approximately 37% compared to the case of a small playout buffer size.

Information Centric Networking (ICN) is a new networking paradigm in which the network provides users with named content, instead of communication channels between hosts. However, many issues, such as naming, routing, resource control, and security, still need to be resolved before it can be realized practically. Further, the energy efficiency of ICNs has not been sufficiently considered. In this study, we evaluate the energy consumption of an ICN network by first measuring the power consumption of actual PCs/smartphones and constructing an energy consumption model. Then, by using the model and the simulator ndnSIM, we calculate the energy consumed by the network. We also evaluate the energy consumed for video delivery, with a view toward power-saving video delivery.

In order to tackle recent mobile traffic explosion, content offloading using Wifi access points from cellular network is deployed but the Wifi system itself is sometimes heavily congested. In this paper, we propose a content offloading system using transportation systems such as a train. In addition, we aim to incorporate CCN (Content Centric Networking) into our system to exploit its in-network caching capability. We compare three protocol designs, pure IP, IP+CCN hybrid and pure CCN, and show the efficiency of our system using a network simulator.

Providing robust content delivery service, such as Video on Demand, with efficient wireless resource usage is important for mobile users. To achieve this, we have proposed a proactive content caching scheme utilizing transportation systems (e.g., train). Because our previous prototype is immature and has a scalability issue, in this paper, we introduce an efficient transmission mechanism of Interest messages to improve throughput efficiency in mobile NDN, develop two prototypes based on NDN and HTTP, and then evaluate prototype performances by performing larger-scale field experiments on actual commercial railroad line. Evaluations conclude that our system can achieve high efficient content transfer and fully utilize wireless network bandwidth. As a result, our system can provide high-quality video streaming for up to 50 users simultaneously.

Improving Quality of Service (QoS) in wireless networks is important and necessary for mobile users. We have previously proposed Comfort Route (CR) Navigation, which navigates users to their destinations using high QoS communication areas, such as Wi-Fi APs, rather than the geographical Shortest Route (SR). In this paper, we employ an analytical model to estimate the CR gain in a theoretical manner which assumes that available cellular and Wi-Fi throughputs are uniform within their coverage. The CR gain is computed by using basic parameters, including wireless network bandwidth and transmission time. To validate our model, we compare simulation results and real observation. These results conclude that the CR gain could estimate by using our analytical model.

With the increasing demand for video streaming in mobile environment, we have been tackling a research on throughput prediction based on the measurement history data in order to improve service quality[1][2]. Since throughput fluctuation occurs sharply in mobile environment, useless packet loss or congestion occurs when available network bandwidth is insufficient compared to the bit rate of video contents. Hence, it is possible to improve the quality of service by deducing the available throughput and selecting appropriate bit rate for the video contents. In this paper, we propose a throughput prediction method that considers mobile locations of a train and their throughput history and show promising experimental results.

Extending battery life for smartphones while using wireless networks is important. In this paper, we propose Comfort Route for Energy Saving (CRFES), which helps users extend the battery life of their smartphones by navigating to their destinations via Quality of Service (QoS)-compliant energy-efficient spots such as Wi-Fi spots. To create CRFES, we construct maps of Wi-Fi spots using logging software we developed for Android phones. In addition to locations, we record actual throughput and energy consumption observed in cellular/Wi-Fi networks. So comfort route navigation is done by choosing a route which minimizes energy consumption instead of choosing the shortest path. Evaluations are carried out by computer simulation and one real city map. The results conclude that CRFES can save the battery life by approximately 50% by spending 1.6 times time cost to reach their destinations on average.

Providing robust content delivery along with efficient wireless resource usage is important for next generation wireless networks. To achieve this, we propose a proactive content caching scheme utilizing transportation systems, especially trains. In our system, we place content servers with CCN capability to every train and station. Segments of video contents are pre-cached by the station servers before trains arrive at stations. Trains receive the contents via high-speed wireless transport while they stop at the stations. We develop a prototype system based on IP and CCN Hybrid protocols. We evaluate its performance by field experiment and compare with traditional CDN scenarios using cellular networks. Evaluations conclude that our system can achieve high-speed and high-reliable video delivery without freezing.

To improve Quality of Service in wireless networks while mobile users travel, we introduce Comfort Route Navigation (CRN) which is a navigation system based on a user centric mobility management for next generation wireless networks. CRN provides an optimal route that satisfies user needs, such as obtaining maximum wireless resources. To achieve CRN, we construct an access point (AP) map of Shinjuku city. To reflect quality of APs in this map, we evaluate throughputs at seven public Wi-Fi spots. Based on these observations, we evaluate performance of our CRN via computer simulations and in a real environment. These evaluations conduct that a CRN user could obtain higher communication quality rather than a Shortest Route (SR) user. These evaluations also conclude that CRN gain depends heavily on the quality and location of the best broadband spot.

Previous our research has introduced an optimal longcut route problem and a number of solutions to this problem. However, to date, the literature has only presented evaluations based on a single theoretical radio propagation model. Because throughput characteristics derived from radio propagation models significantly affects longcut route performance, this study introduces three simple models by referring to actual measurement values. The results of our evaluation show that there are large differences in performance between the three models. The Non-Line-of-Sight model can have an improvement ratio approximately five times greater than that of the Line-of-Sight Indoor model and six times greater than that of the previous theoretical model. © 2012 IEEE.

In this paper, we propose a "comfort route" navigation system with proactive content delivery and an offloading scheme for mobile users. To search an optimal path for highly reliable video streaming, we apply a delivery scheduling algorithm, which has been proposed for proactive content delivery scheme, to a path-finding problem. In preliminary evaluation, the result concludes that our approach can drastically reduce transmission time rather than a cellular usage case. This is because the mobile user can communicate with only high-speed hot spots. So, we expect that our approach could also save user's mobile battery life.

In this paper, we evaluate Quality of Service characteristics of MPEG-DASH using 4K video over various wireless networks. We also evaluate the effects of various environments, such as the number of users who share the same wireless channel or bandwidth and users' behaviors (e.g. still, walking, and moving on subway), over Long Term Evolution (LTE), 2.4 GHz and 5 GHz Wi-Fi networks. The results conclude that, in case of LTE and 5 GHz Wi-Fi networks, mobile users enjoy high quality 4K video without any video playback interruption in any situations, even on subway. However, in case of 2.4 GHz Wi-Fi networks, mobile users are forced to experience very low quality video with long video playback freeze. This is because network quality of 2.4GHz Wi-Fi network is quite low due to interference waves from other access points of which wireless channels are severely overlapped.

In this paper, we propose an adaptive buffer control technique for robust video streaming. Our approach has two elements which are called "long-term throughput prediction" and "guaranteed playout buffer filling." Since network quality degradation can be detected by using long-term throughput prediction, playout buffer also can be adaptively and proactively controlled. To avoid any video playback interruption due to the network quality degradation, our approach calculates the optimal amount of playout buffer and schedules video download timing in a theoretical manner. We evaluate its performance in real environment and then validate the effectiveness of our proposal.

We evaluated various characteristics of 4K video streaming using DASH-JS over LTE, public Wi-Fi services and WiMAX. We did experiments while stopping, walking, and riding the train. We found that a mobile user could enjoy smooth video streaming by using these networks even 4K video.

We propose proactive content caching scheme utilizing transportation systems for robust video streaming with efficient wireless resources usage. In this paper, we develop a prototype system and evaluate its performances by using actual train vehicles and computer simulations. Evaluations conclude that our system can achieve higher-speed and highly-reliable video streaming rather than traditional CDN scenarios using cellular networks.

We propose proactive content caching scheme utilizing transportation systems for robust video streaming with efficient wireless resources usage. In this paper, we develop a prototype system and evaluate its performances by using actual train vehicles and computer simulations. Evaluations conclude that our system can achieve higher-speed and highly-reliable video streaming rather than traditional CDN scenarios using cellular networks.

We have introduced a proactive content delivery system utilizing transportation systems for next generation mobile networks. In this paper, we propose a proactive delivery scheduling, which is called "Smart Scheduler." This scheduler calculates contents quality and quantity and also selects delivery location and timing. To validate and evaluate our method, we experiment in the real environments. Experimental results conduct that our method performs effectively.

We have already introduced an analytical model for Comfort Route (CR) Navigation providing high communication quality routes to users. In this paper, to estimate CR performance in more realistic environment, we modify the previous analytical model to Heterogeneous Network (HetNet). To validate our model, we employ several scenarios in real experiments. We evaluate throughputs characteristics at ten public Wi-Fi spots in Takadanobaba, which is a city near to our university. We compare the results in real experiments and prove that our model works well with high accuracy.

Recently, extending battery life of mobile devices is important for mobile users. We have already introduced Comfort Route (CR) Navigation with energy saving. In this paper, to estimate energy saving effect in a theoretical manner, we employ the analytical model for this route. We find that the effect depends on four basic parameters, such as cellular and Wi-Fi throughput, energy consumption for cellular, Wi-Fi, and idle states, CR and Shortest route cellular usage ratio, and CR longcut ratio. To validate our model, we compare to simulated results and prove that our model works well with high accuracy.

Recently, improving Quality of Service (QoS) in wireless networks is important for mobile users. We have already introduced Comfort Route (CR) Navigation providing high communication quality. In this paper, to estimate CR performance in a theoretical manner, we employ the analytical model for CR. To validate our model, we compare to simulated results and prove that our model works well with high accuracy. We find that the CR gain depends on four basic parameters, such as the cellular and Wi-Fi throughput ratio, the shortest route Wi-Fi coverage area, the CR cellular usage ratio, and user cost which specifies longcut margin. We also find that the best CR gain strongly depends on the cellular and Wi-Fi throughput ratio.

Recently, the demand for visualizing congestion levels of Wi-Fi spots is increasing because Wi-Fi spots are said to be quite congested. In this paper. for this purpose, we employ a simple approximation formula about the relationship between throughputs and the number of active users associated with the access point. In addition, we try to estimate fiiture throughput variation which depends on fluctuation of the associated users using a state transition model. Then, we classify the congestion level into four categories depending on the current and future expected throughputs and evaluate the accuracy of our proposal. We found that our proposal shows acceptable accuracy rate of 70〜90% in our experimental conditions.

Recently, Internet services and wireless infrastructures have grown widely and diversely, and people can easily access Internet anytime and anywhere. Smartphones have become an indispensable part for this purpose, so people are very nervous about smartphone's battery life. In this paper, we therefore propose Comfort Route for Energy Saving (CRFES) which helps users reach their destinations while stopping by Quality of Service (QoS) fulfilled low-power communication places such as Wi-Fi spots. We evaluate the performance of CRFES in a computer simulation and a real environment. We find that CRFES can save mobile device batteries drastically by using Wi-Fi rather than cellulars with small extra time cost to visit hot spots.

The demand for access to communication while in transit is increasing, especially among mobile device users. Previous our research has introduced an optimal route problem and a number of solutions to this problem. However, to date, the literature has only presented evaluations based on a theoretical model. In this paper, to validate and enhance the previous work, we observe actual throughput characteristics in public wireless LAN services such as FreeSpot and DOCOMO 's Mzone. We evaluate an optimal longcut route navigation using the results of these observations and show its effectiveness in computer simulations and experiments in actual environment. In both experiments, the optimal longcut route can gain data amount approximately 15 times greater than that of a short cut route.