Waqas Rahman - Academia.edu (original) (raw)

Papers by Waqas Rahman

Multimedia Tools and Applications

The traditional client-based HTTP adaptation strategies do not explicitly coordinate between the ... more The traditional client-based HTTP adaptation strategies do not explicitly coordinate between the clients, servers, and cellular networks. A lack of coordination leads to suboptimal user experience. In addition to optimizing Quality of Experience (QoE), other challenges in adapting HTTP adaptive streaming (HAS) to the cellular environment are overcoming unfair allocation of the video rate and inefficient utilization of the bandwidth under the high-dynamics cellular links. Furthermore, the majority of the adaptive strategies ignore important video content characteristics and HAS client information, such as segment duration, buffer size, and video duration, in the video quality selection process. In this paper, we present a content-aware hybrid multi-access edge computing (MEC)-assisted quality adaptation algorithm by taking advantage of the capabilities of edge cloud computing. The proposed algorithm exploits video content characteristics, HAS client settings, and application-layer in...

2018 International Conference on Information Networking (ICOIN), 2018

In this paper, we propose a rate adaptation algorithm for Hypertext Transfer Protocol (HTTP) stre... more In this paper, we propose a rate adaptation algorithm for Hypertext Transfer Protocol (HTTP) streaming. The proposed algorithm selects the video bit rates based on the estimated throughput and playback buffer occupancy. The performance of the algorithm is compared with that of throughput- and buffer-based adaptation algorithms. The proposed method selects high-quality video segments, while minimizing video quality changes and the risk of playback interruption. We evaluate the algorithm for single- and multi-user environments and demonstrate that it performs remarkably well under varying network conditions. Furthermore, we determine that it efficiently utilizes network throughput to achieve a high video rate; competing HTTP clients achieve equitable video rates. We also confirm that the proposed algorithm performs well regardless of the buffer size and segment duration.

KIISE Transactions on Computing Practices

Journal of Visual Communication and Image Representation, 2022

2020 International Conference on Information Networking (ICOIN), 2020

Multi-Access Edge Computing (MEC) adaptive streaming presents an opportunity to jointly optimize ... more Multi-Access Edge Computing (MEC) adaptive streaming presents an opportunity to jointly optimize the quality of experience in cellular networks by moving the adaptation intelligence from the client to the edge cloud. In this paper, we investigate the performance of MEC-assisted algorithms and compare their performance with the client based adaptation logic. We conduct extensive experiments and quantify benefits and drawbacks of edge computing-assisted adaptation algorithms. The results from our experiments reveal that MEC-assisted algorithms outperforms the purely clientbased heuristics in most of the video quality metrics. However, the results also show that the MEC-assisted algorithms are not able to protect the playback buffer from drying up under different network settings.

The American Mathematical Monthly, 1984

• A CTMC is said to be irreducible if every state can be reached from every other state, with a n... more • A CTMC is said to be irreducible if every state can be reached from every other state, with a non-zero probability. In a finite state irreducible CTMC all states are positive recurrent. • A state is said to be absorbing if no other state can be reached from it with non-zero probability. • Notion of transient, recurrent non-null, recurrent null are the same as in a DTMC. There is no notion of periodicity in a CTMC, however. • Unless otherwise specified, when we say CTMC, we mean HCTMC Copyright © 2006 by K.S. Trivedi Communicating classes • Communicating states: i and j are said to be communicating if there exist directed paths from i and j and from j and i. • Closed set of states: A commutating set of states C forms a closed set, if no state outside of C can be reached from any state in C.

IEEE Access

Most studies on adaptive streaming over Hypertext Transport Protocol (HTTP) have focused on impro... more Most studies on adaptive streaming over Hypertext Transport Protocol (HTTP) have focused on improving the quality of experience (QoE) of clients by running the rate adaptation algorithm on the client side. In a cellular environment, this leads to inefficient resource utilization because of the lack of coordination between the competing clients. In cellular networks, the key challenge for HTTP adaptive streaming (HAS) is to optimize the conflicting video quality objectives. Edge cloud-assisted adaptive streaming presents an opportunity to optimize the quality of experience in cellular networks by moving the adaptation intelligence from the client to the edge cloud. HAS algorithms select the video quality based on the estimated throughput and playback buffer level. In this paper, we first present a joint throughput estimation method for HAS by taking advantage of mobile edge computing. Next, we present an optimized solution for multi-access edge computing (MEC)-assisted HAS by using edge cloud capabilities. Due to the non-deterministic polynomial-time hardness of the problem, we design a heuristic rate adaptation algorithm to jointly enhance the quality metrics of the competing clients. Our extension simulation results show that the proposed edge cloud-assisted rate adaptation algorithm outperforms the existing strategies under different client-side and server-side settings. Furthermore, we show that the proposed algorithm is promising under slow-moving and fast-moving environments.

To execute computation-intensive applications and stringent latency-critical tasks at resource co... more To execute computation-intensive applications and stringent latency-critical tasks at resource constraints smart mobile devices, mobile edge computing (MEC) in small-cell networks is one of the leading thought, where mobile devices will offload their computation-intensive tasks to the adjacent small-cell network for faster processing. Currently, some research work has been done for combining mobile edge computing and small-cell networks together. Existing researches mostly concentrate on the user to small base station (SBS) offloading and improving the radio access performance using optimization, while the computing capability of SBS-MEC server is ignored. In order to acquire superior performance, an efficient orchestration-based task offloading for mobile edge computing in small-cell networks is proposed in this paper where edge orchestrator collects all the information from the neighboring small-cell SBS-MEC server to decide for forwarding the workloads from overloaded SBS-MEC to ...

2018 International Conference on Information and Communication Technology Convergence (ICTC), 2018

This paper presents a quality adaptation algorithm for multimedia streaming over constrained appl... more This paper presents a quality adaptation algorithm for multimedia streaming over constrained application protocol (CoAP) in the Internet of Things (IoT) environment. The proposed scheme is designed to improve user experience by meeting the video quality objectives in a constrained wireless environment. To achieve this, the proposed scheme increases or decreases the video quality by taking the throughput of the available wireless link and segment download time into consideration. The algorithm decides to aggressively or conservatively increase the video quality based on the sizes of the upcoming segments. In addition, the algorithm decreases the video rate by predicting the buffer level at the download of the next segment. The results show that the proposed algorithm improves user experience by achieving high quality video and mitigating the risk of playback interruptions.

Applied Sciences, 2021

Video clients employ HTTP-based adaptive bitrate (ABR) algorithms to optimize users’ quality of e... more Video clients employ HTTP-based adaptive bitrate (ABR) algorithms to optimize users’ quality of experience (QoE). ABR algorithms adopt video quality based on the network conditions during playback. The existing state-of-the-art ABR algorithms ignore the fact that video streaming services deploy segment durations differently in different services, and HTTP clients offer distinct buffer sizes. The existing ABR algorithms use fixed control laws and are designed with predefined client/server settings. As a result, adaptation algorithms fail to achieve optimal performance across a variety of video client settings and QoE objectives. We propose a buffer- and segment-aware fuzzy-based ABR algorithm that selects video rates for future video segments based on segment duration and the client’s buffer size in addition to throughput and playback buffer level. We demonstrate that the proposed algorithm guarantees high QoE across various video player settings and video content characteristics. Th...

Video streaming services make up a large proportion of Internet traffic throughout the world. Ada... more Video streaming services make up a large proportion of Internet traffic throughout the world. Adaptive streaming allows for dynamical adaptation of the video bitrate with varying network conditions, to guarantee the best user experience. We propose an adaptive bitrate scheme that intelligently selects the video bitrates based on the estimated throughput and buffer occupancy. We show that the proposed algorithm selects a high playback video rate and avoids unnecessary rebuffering while keeping a low frequency of video rate changes. KeywordsRate adaptation; Quality adaptation; Quality of Experience; HTTP Streaming; Multimedia

Multi-Access Edge Computing (MEC) adaptive streaming presents an opportunity to jointly optimize ... more Multi-Access Edge Computing (MEC) adaptive streaming presents an opportunity to jointly optimize the quality of experience in cellular networks by moving the adaptation intelligence from the client to the edge cloud. In this paper, we investigate the performance of MEC-assisted algorithms and compare their performance with the client based adaptation logic. We conduct extensive experiments and quantify benefits and drawbacks of edge computing-assisted adaptation algorithms. The results from our experiments reveal that MEC-assisted algorithms outperforms the purely client-based heuristics in most of the video quality metrics. However, the results also show that the MEC-assisted algorithms are not able to protect the playback buffer from drying up under different network settings.

KSII Transactions on Internet and Information Systems, 2015

Video streaming services make up a large proportion of Internet traffic on both fixed and mobile ... more Video streaming services make up a large proportion of Internet traffic on both fixed and mobile access throughout the world. Adaptive streaming allows for dynamical adaptation of the bitrate with varying network conditions, to guarantee the best user experience. Adaptive bitrate algorithms face a significant challenge in correctly estimating the throughput as it varies widely over time. In this paper, we first evaluate the throughput estimation techniques and show that the method that we have used offers stable response to throughput fluctuations while maintaining a stable playback buffer. Then, we propose an adaptive bitrate scheme that intelligently selects the video bitrates based on the estimated throughput and buffer occupancy. We show that the proposed scheme improves viewing experience by achieving a high video rate without taking unnecessary risks and by minimizing the frequency of changes in the video quality. Furthermore, we show that it offers a stable response to short-term fluctuations and responds swiftly to large fluctuations. We evaluate our algorithm for both constant bitrate (CBR) and variable bitrate (VBR) video content by taking into account the segment sizes and show that it significantly improves the quality of video streaming.

IEEE Access, 2019

Most studies on adaptive streaming over Hypertext Transport Protocol (HTTP) have focused on impro... more Most studies on adaptive streaming over Hypertext Transport Protocol (HTTP) have focused on improving the quality of experience (QoE) of clients by running the rate adaptation algorithm on the client side. In a cellular environment, this leads to inefficient resource utilization because of the lack of coordination between the competing clients. In cellular networks, the key challenge for HTTP adaptive streaming (HAS) is to optimize the conflicting video quality objectives. Edge cloud-assisted adaptive streaming presents an opportunity to optimize the quality of experience in cellular networks by moving the adaptation intelligence from the client to the edge cloud. HAS algorithms select the video quality based on the estimated throughput and playback buffer level. In this paper, we first present a joint throughput estimation method for HAS by taking advantage of mobile edge computing. Next, we present an optimized solution for multi-access edge computing (MEC)-assisted HAS by using edge cloud capabilities. Due to the non-deterministic polynomial-time hardness of the problem, we design a heuristic rate adaptation algorithm to jointly enhance the quality metrics of the competing clients. Our extension simulation results show that the proposed edge cloud-assisted rate adaptation algorithm outperforms the existing strategies under different client-side and server-side settings. Furthermore, we show that the proposed algorithm is promising under slow-moving and fast-moving environments.

Applied Sciences

Accelerating the development of the 5G network and Internet of Things (IoT) application, multi-ac... more Accelerating the development of the 5G network and Internet of Things (IoT) application, multi-access edge computing (MEC) in a small-cell network (SCN) is designed to provide computation-intensive and latency-sensitive applications through task offloading. However, without collaboration, the resources of a single MEC server are wasted or sometimes overloaded for different service requests and applications; therefore, it increases the user’s task failure rate and task duration. Meanwhile, the distinct MEC server has faced some challenges to determine where the offloaded task will be processed because the system can hardly predict the demand of end-users in advance. As a result, the quality-of-service (QoS) will be deteriorated because of service interruptions, long execution, and waiting time. To improve the QoS, we propose a novel Fuzzy logic-based collaborative task offloading (FCTO) scheme in MEC-enabled densely deployed small-cell networks. In FCTO, the delay sensitivity of the ...

Multimedia Systems

Adaptive streaming allows for dynamic adaptation of the bitrate to varying network conditions, to... more Adaptive streaming allows for dynamic adaptation of the bitrate to varying network conditions, to guarantee the best user experience. Adaptive bitrate algorithms face a significant challenge in correctly estimating the throughput, as the throughput varies widely over time. The current throughput estimation methods cannot distinguish between throughput fluctuations of different amplitude and frequency. In this paper, we propose a throughput estimation method that accurately estimates the throughput based on previous throughput samples. It is robust to short term and small fluctuations, and sensitive to large fluctuations in throughput. Furthermore, we propose a rate adaptive algorithm for video on demand (VoD) services that selects the quality of the video based on the estimated throughput and playback buffer occupancy. The objective of the rate adaptive algorithms is to guarantee high video quality to improve user experience. The proposed algorithm dynamically adjusts the quality level of the video stream. The proposed method selects high quality video segments, while minimizing the risk of playback interruption. Furthermore, the proposed method minimizes the frequency of video rate changes. We show that the algorithm smoothly switches the video rate to improve user experience. Furthermore, we determine that it efficiently utilizes network resources to achieve a high video rate; competing HTTP clients achieve equitable video rates. We also confirm that variations in the playback buffer size and segment duration do not affect the performance of the proposed algorithm.

Journal of Visual Communication and Image Representation

Abstract In this paper, we propose an estimation method that estimates the throughput of upcoming... more Abstract In this paper, we propose an estimation method that estimates the throughput of upcoming video segments based on variations in the network throughput observed during the download of previous video segments. Then, we propose a rate-adaptive algorithm for Hypertext Transfer Protocol (HTTP) streaming. The proposed algorithm selects the quality of the video based on the estimated throughput and playback buffer occupancy. The proposed method selects high-quality video segments, while minimizing video quality changes and the risk of playback interruption, improving user’s experience. We evaluate the algorithm for single- and multi-user environments and demonstrate that it performs remarkably well under varying network conditions. Furthermore, we determine that it efficiently utilizes network resources to achieve a high video rate; competing HTTP clients achieve equitable video rates. We also confirm that variations in the playback buffer size and segment duration do not affect the performance of the proposed algorithm.

Multimedia Tools and Applications

The traditional client-based HTTP adaptation strategies do not explicitly coordinate between the ... more The traditional client-based HTTP adaptation strategies do not explicitly coordinate between the clients, servers, and cellular networks. A lack of coordination leads to suboptimal user experience. In addition to optimizing Quality of Experience (QoE), other challenges in adapting HTTP adaptive streaming (HAS) to the cellular environment are overcoming unfair allocation of the video rate and inefficient utilization of the bandwidth under the high-dynamics cellular links. Furthermore, the majority of the adaptive strategies ignore important video content characteristics and HAS client information, such as segment duration, buffer size, and video duration, in the video quality selection process. In this paper, we present a content-aware hybrid multi-access edge computing (MEC)-assisted quality adaptation algorithm by taking advantage of the capabilities of edge cloud computing. The proposed algorithm exploits video content characteristics, HAS client settings, and application-layer in...

2018 International Conference on Information Networking (ICOIN), 2018

In this paper, we propose a rate adaptation algorithm for Hypertext Transfer Protocol (HTTP) stre... more In this paper, we propose a rate adaptation algorithm for Hypertext Transfer Protocol (HTTP) streaming. The proposed algorithm selects the video bit rates based on the estimated throughput and playback buffer occupancy. The performance of the algorithm is compared with that of throughput- and buffer-based adaptation algorithms. The proposed method selects high-quality video segments, while minimizing video quality changes and the risk of playback interruption. We evaluate the algorithm for single- and multi-user environments and demonstrate that it performs remarkably well under varying network conditions. Furthermore, we determine that it efficiently utilizes network throughput to achieve a high video rate; competing HTTP clients achieve equitable video rates. We also confirm that the proposed algorithm performs well regardless of the buffer size and segment duration.

KIISE Transactions on Computing Practices

Journal of Visual Communication and Image Representation, 2022

2020 International Conference on Information Networking (ICOIN), 2020

Multi-Access Edge Computing (MEC) adaptive streaming presents an opportunity to jointly optimize ... more Multi-Access Edge Computing (MEC) adaptive streaming presents an opportunity to jointly optimize the quality of experience in cellular networks by moving the adaptation intelligence from the client to the edge cloud. In this paper, we investigate the performance of MEC-assisted algorithms and compare their performance with the client based adaptation logic. We conduct extensive experiments and quantify benefits and drawbacks of edge computing-assisted adaptation algorithms. The results from our experiments reveal that MEC-assisted algorithms outperforms the purely clientbased heuristics in most of the video quality metrics. However, the results also show that the MEC-assisted algorithms are not able to protect the playback buffer from drying up under different network settings.

The American Mathematical Monthly, 1984

• A CTMC is said to be irreducible if every state can be reached from every other state, with a n... more • A CTMC is said to be irreducible if every state can be reached from every other state, with a non-zero probability. In a finite state irreducible CTMC all states are positive recurrent. • A state is said to be absorbing if no other state can be reached from it with non-zero probability. • Notion of transient, recurrent non-null, recurrent null are the same as in a DTMC. There is no notion of periodicity in a CTMC, however. • Unless otherwise specified, when we say CTMC, we mean HCTMC Copyright © 2006 by K.S. Trivedi Communicating classes • Communicating states: i and j are said to be communicating if there exist directed paths from i and j and from j and i. • Closed set of states: A commutating set of states C forms a closed set, if no state outside of C can be reached from any state in C.

IEEE Access

Most studies on adaptive streaming over Hypertext Transport Protocol (HTTP) have focused on impro... more Most studies on adaptive streaming over Hypertext Transport Protocol (HTTP) have focused on improving the quality of experience (QoE) of clients by running the rate adaptation algorithm on the client side. In a cellular environment, this leads to inefficient resource utilization because of the lack of coordination between the competing clients. In cellular networks, the key challenge for HTTP adaptive streaming (HAS) is to optimize the conflicting video quality objectives. Edge cloud-assisted adaptive streaming presents an opportunity to optimize the quality of experience in cellular networks by moving the adaptation intelligence from the client to the edge cloud. HAS algorithms select the video quality based on the estimated throughput and playback buffer level. In this paper, we first present a joint throughput estimation method for HAS by taking advantage of mobile edge computing. Next, we present an optimized solution for multi-access edge computing (MEC)-assisted HAS by using edge cloud capabilities. Due to the non-deterministic polynomial-time hardness of the problem, we design a heuristic rate adaptation algorithm to jointly enhance the quality metrics of the competing clients. Our extension simulation results show that the proposed edge cloud-assisted rate adaptation algorithm outperforms the existing strategies under different client-side and server-side settings. Furthermore, we show that the proposed algorithm is promising under slow-moving and fast-moving environments.

To execute computation-intensive applications and stringent latency-critical tasks at resource co... more To execute computation-intensive applications and stringent latency-critical tasks at resource constraints smart mobile devices, mobile edge computing (MEC) in small-cell networks is one of the leading thought, where mobile devices will offload their computation-intensive tasks to the adjacent small-cell network for faster processing. Currently, some research work has been done for combining mobile edge computing and small-cell networks together. Existing researches mostly concentrate on the user to small base station (SBS) offloading and improving the radio access performance using optimization, while the computing capability of SBS-MEC server is ignored. In order to acquire superior performance, an efficient orchestration-based task offloading for mobile edge computing in small-cell networks is proposed in this paper where edge orchestrator collects all the information from the neighboring small-cell SBS-MEC server to decide for forwarding the workloads from overloaded SBS-MEC to ...

2018 International Conference on Information and Communication Technology Convergence (ICTC), 2018

This paper presents a quality adaptation algorithm for multimedia streaming over constrained appl... more This paper presents a quality adaptation algorithm for multimedia streaming over constrained application protocol (CoAP) in the Internet of Things (IoT) environment. The proposed scheme is designed to improve user experience by meeting the video quality objectives in a constrained wireless environment. To achieve this, the proposed scheme increases or decreases the video quality by taking the throughput of the available wireless link and segment download time into consideration. The algorithm decides to aggressively or conservatively increase the video quality based on the sizes of the upcoming segments. In addition, the algorithm decreases the video rate by predicting the buffer level at the download of the next segment. The results show that the proposed algorithm improves user experience by achieving high quality video and mitigating the risk of playback interruptions.

Applied Sciences, 2021

Video clients employ HTTP-based adaptive bitrate (ABR) algorithms to optimize users’ quality of e... more Video clients employ HTTP-based adaptive bitrate (ABR) algorithms to optimize users’ quality of experience (QoE). ABR algorithms adopt video quality based on the network conditions during playback. The existing state-of-the-art ABR algorithms ignore the fact that video streaming services deploy segment durations differently in different services, and HTTP clients offer distinct buffer sizes. The existing ABR algorithms use fixed control laws and are designed with predefined client/server settings. As a result, adaptation algorithms fail to achieve optimal performance across a variety of video client settings and QoE objectives. We propose a buffer- and segment-aware fuzzy-based ABR algorithm that selects video rates for future video segments based on segment duration and the client’s buffer size in addition to throughput and playback buffer level. We demonstrate that the proposed algorithm guarantees high QoE across various video player settings and video content characteristics. Th...

Video streaming services make up a large proportion of Internet traffic throughout the world. Ada... more Video streaming services make up a large proportion of Internet traffic throughout the world. Adaptive streaming allows for dynamical adaptation of the video bitrate with varying network conditions, to guarantee the best user experience. We propose an adaptive bitrate scheme that intelligently selects the video bitrates based on the estimated throughput and buffer occupancy. We show that the proposed algorithm selects a high playback video rate and avoids unnecessary rebuffering while keeping a low frequency of video rate changes. KeywordsRate adaptation; Quality adaptation; Quality of Experience; HTTP Streaming; Multimedia

Multi-Access Edge Computing (MEC) adaptive streaming presents an opportunity to jointly optimize ... more Multi-Access Edge Computing (MEC) adaptive streaming presents an opportunity to jointly optimize the quality of experience in cellular networks by moving the adaptation intelligence from the client to the edge cloud. In this paper, we investigate the performance of MEC-assisted algorithms and compare their performance with the client based adaptation logic. We conduct extensive experiments and quantify benefits and drawbacks of edge computing-assisted adaptation algorithms. The results from our experiments reveal that MEC-assisted algorithms outperforms the purely client-based heuristics in most of the video quality metrics. However, the results also show that the MEC-assisted algorithms are not able to protect the playback buffer from drying up under different network settings.

KSII Transactions on Internet and Information Systems, 2015

Video streaming services make up a large proportion of Internet traffic on both fixed and mobile ... more Video streaming services make up a large proportion of Internet traffic on both fixed and mobile access throughout the world. Adaptive streaming allows for dynamical adaptation of the bitrate with varying network conditions, to guarantee the best user experience. Adaptive bitrate algorithms face a significant challenge in correctly estimating the throughput as it varies widely over time. In this paper, we first evaluate the throughput estimation techniques and show that the method that we have used offers stable response to throughput fluctuations while maintaining a stable playback buffer. Then, we propose an adaptive bitrate scheme that intelligently selects the video bitrates based on the estimated throughput and buffer occupancy. We show that the proposed scheme improves viewing experience by achieving a high video rate without taking unnecessary risks and by minimizing the frequency of changes in the video quality. Furthermore, we show that it offers a stable response to short-term fluctuations and responds swiftly to large fluctuations. We evaluate our algorithm for both constant bitrate (CBR) and variable bitrate (VBR) video content by taking into account the segment sizes and show that it significantly improves the quality of video streaming.

IEEE Access, 2019

Most studies on adaptive streaming over Hypertext Transport Protocol (HTTP) have focused on impro... more Most studies on adaptive streaming over Hypertext Transport Protocol (HTTP) have focused on improving the quality of experience (QoE) of clients by running the rate adaptation algorithm on the client side. In a cellular environment, this leads to inefficient resource utilization because of the lack of coordination between the competing clients. In cellular networks, the key challenge for HTTP adaptive streaming (HAS) is to optimize the conflicting video quality objectives. Edge cloud-assisted adaptive streaming presents an opportunity to optimize the quality of experience in cellular networks by moving the adaptation intelligence from the client to the edge cloud. HAS algorithms select the video quality based on the estimated throughput and playback buffer level. In this paper, we first present a joint throughput estimation method for HAS by taking advantage of mobile edge computing. Next, we present an optimized solution for multi-access edge computing (MEC)-assisted HAS by using edge cloud capabilities. Due to the non-deterministic polynomial-time hardness of the problem, we design a heuristic rate adaptation algorithm to jointly enhance the quality metrics of the competing clients. Our extension simulation results show that the proposed edge cloud-assisted rate adaptation algorithm outperforms the existing strategies under different client-side and server-side settings. Furthermore, we show that the proposed algorithm is promising under slow-moving and fast-moving environments.

Applied Sciences

Accelerating the development of the 5G network and Internet of Things (IoT) application, multi-ac... more Accelerating the development of the 5G network and Internet of Things (IoT) application, multi-access edge computing (MEC) in a small-cell network (SCN) is designed to provide computation-intensive and latency-sensitive applications through task offloading. However, without collaboration, the resources of a single MEC server are wasted or sometimes overloaded for different service requests and applications; therefore, it increases the user’s task failure rate and task duration. Meanwhile, the distinct MEC server has faced some challenges to determine where the offloaded task will be processed because the system can hardly predict the demand of end-users in advance. As a result, the quality-of-service (QoS) will be deteriorated because of service interruptions, long execution, and waiting time. To improve the QoS, we propose a novel Fuzzy logic-based collaborative task offloading (FCTO) scheme in MEC-enabled densely deployed small-cell networks. In FCTO, the delay sensitivity of the ...

Multimedia Systems

Adaptive streaming allows for dynamic adaptation of the bitrate to varying network conditions, to... more Adaptive streaming allows for dynamic adaptation of the bitrate to varying network conditions, to guarantee the best user experience. Adaptive bitrate algorithms face a significant challenge in correctly estimating the throughput, as the throughput varies widely over time. The current throughput estimation methods cannot distinguish between throughput fluctuations of different amplitude and frequency. In this paper, we propose a throughput estimation method that accurately estimates the throughput based on previous throughput samples. It is robust to short term and small fluctuations, and sensitive to large fluctuations in throughput. Furthermore, we propose a rate adaptive algorithm for video on demand (VoD) services that selects the quality of the video based on the estimated throughput and playback buffer occupancy. The objective of the rate adaptive algorithms is to guarantee high video quality to improve user experience. The proposed algorithm dynamically adjusts the quality level of the video stream. The proposed method selects high quality video segments, while minimizing the risk of playback interruption. Furthermore, the proposed method minimizes the frequency of video rate changes. We show that the algorithm smoothly switches the video rate to improve user experience. Furthermore, we determine that it efficiently utilizes network resources to achieve a high video rate; competing HTTP clients achieve equitable video rates. We also confirm that variations in the playback buffer size and segment duration do not affect the performance of the proposed algorithm.

Journal of Visual Communication and Image Representation

Abstract In this paper, we propose an estimation method that estimates the throughput of upcoming... more Abstract In this paper, we propose an estimation method that estimates the throughput of upcoming video segments based on variations in the network throughput observed during the download of previous video segments. Then, we propose a rate-adaptive algorithm for Hypertext Transfer Protocol (HTTP) streaming. The proposed algorithm selects the quality of the video based on the estimated throughput and playback buffer occupancy. The proposed method selects high-quality video segments, while minimizing video quality changes and the risk of playback interruption, improving user’s experience. We evaluate the algorithm for single- and multi-user environments and demonstrate that it performs remarkably well under varying network conditions. Furthermore, we determine that it efficiently utilizes network resources to achieve a high video rate; competing HTTP clients achieve equitable video rates. We also confirm that variations in the playback buffer size and segment duration do not affect the performance of the proposed algorithm.