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Abstract
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Related Work
PS-Dash Design
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Computer Science
Human-Computer Interaction

Parallel Streaming for a Multi-homed Dash client

Profile image of Ali HODROJAli HODROJ

2020 International Wireless Communications and Mobile Computing (IWCMC)

https://doi.org/10.1109/IWCMC48107.2020.9148354
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6 pages

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Abstract

Nowadays, multimedia streaming traffic reaches 71 % from the mobile data traffic over the world and most of the multimedia services use Dynamic adaptive streaming over HTTP (DASH) to adjust video delivery to the dynamic network environment and achieve higher user Quality of Experience (QoE) levels. Moreover, 90 % of the video traffic is consumed by smart devices equipped with multiple network interfaces (Wifi, 3G, and 4G) known as multi-homed devices. In this paper, we propose a new DASH-parallel streaming solution, which exploits the diversity of access network connections to improve video quality over DASH protocol. Experimental Results show that our proposed approach improves the perceived QoE in different network conditions, without requiring further energy expenditure. Index Terms-MPEG-DASH,multi-homing and parallel streaming.

FAQs

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What are the main benefits of parallel streaming in multi-homed DASH clients?add

Parallel streaming enhances video quality by utilizing multiple connections, achieving 95% average bitrate improvement in 80% of scenarios tested. It also improves robustness by enabling connection switching during congestion or failure.

How does PS-DASH handle packet ordering to enhance video quality?add

PS-DASH prioritizes seamless video transmission by downloading segments from both ends simultaneously, avoiding head-of-line blocking. This intra-segment approach allows for maintaining video integrity despite varying network conditions.

What methodology was used to evaluate the performance of PS-DASH?add

The proposed solution was tested across 192 scenarios simulating diverse network throughput with smooth and rough variations. Key metrics included average video bitrate and buffering time, with results indicating consistent quality enhancement.

When is parallel streaming not utilized in the PS-DASH framework?add

Parallel streaming is minimized when maximum video quality can be achieved through a single connection, reducing energy consumption. Observations showed usage dropped from 70% to 18% in optimal conditions.

How does PS-DASH address energy consumption during video streaming?add

The algorithm actively monitors network conditions and only engages parallel streaming when it offers improved quality, thereby conserving energy. Future upgrades aim to integrate buffer status into energy management strategies.

Figures (9)
Fig. 1. Multihoming Infrastructure This emerging diversity of access networks could be used to improve the perceived video quality. In this paper, we study the use case of a multi-homed DASH client that aims to watch a video content replicated in many DASH servers, as shown in Figure 1. For this pur- pose, a parallel streaming mechanism over DASH is proposed to enhance the QoE for multi-homed clients. Our solution permits the client to request a segment simultaneously using both available connections, to provide a kind of bandwidth aggregation mechanism that maximizes the available resources (connections), which allow improving the received video-rate without any quality degradation due to the variation of the network’s conditions on both connections. Additionally, it provides the following features:
Fig. 1. Multihoming Infrastructure This emerging diversity of access networks could be used to improve the perceived video quality. In this paper, we study the use case of a multi-homed DASH client that aims to watch a video content replicated in many DASH servers, as shown in Figure 1. For this pur- pose, a parallel streaming mechanism over DASH is proposed to enhance the QoE for multi-homed clients. Our solution permits the client to request a segment simultaneously using both available connections, to provide a kind of bandwidth aggregation mechanism that maximizes the available resources (connections), which allow improving the received video-rate without any quality degradation due to the variation of the network’s conditions on both connections. Additionally, it provides the following features:
The main concept of DASH is the adaptive bitrate algorithm that permits the user to switch between the available video rates according to its network conditions and other information that affects the video performances (e.g. like the buffer level). Using multi-homing affects these rules and leads in some cases to quality degradation. Indeed, the decision on the choice of the segment’s quality can be based on the conditions of another interface, with a very different status. Our smart segment downloader doesn’t suffer from this problem as it is working in intra-segment, which means that it doesn’t affect the measurements of the available bitrate (ABR). Thus, it is based on the whole segment not a part of the segment.
The main concept of DASH is the adaptive bitrate algorithm that permits the user to switch between the available video rates according to its network conditions and other information that affects the video performances (e.g. like the buffer level). Using multi-homing affects these rules and leads in some cases to quality degradation. Indeed, the decision on the choice of the segment’s quality can be based on the conditions of another interface, with a very different status. Our smart segment downloader doesn’t suffer from this problem as it is working in intra-segment, which means that it doesn’t affect the measurements of the available bitrate (ABR). Thus, it is based on the whole segment not a part of the segment.
Fig. 3. Experiment Lab Testbed Setup
Fig. 3. Experiment Lab Testbed Setup
Fig. 5. Video quality variation in function of network speed variation
Fig. 5. Video quality variation in function of network speed variation
Fig. 4. Video quality variation in function of Throughput variations
Fig. 4. Video quality variation in function of Throughput variations
Fig. 7. Average Bitrate Enhancement of MP-DASH Algorithm Figure 7 shows the Cumulative Distributed Function (CDF) of the average enhancement calculated from these sets of “xperiments, when comparing our algorithm using the Parallel Streaming over DASH to the standard Dash (single transmis- sion) using the highest connection.The result shows quality enhancement between 10% to 95% in 80% from the scenarios, while 10% from the 192 scenarios were subject to quality degradation in comparison to single transmission. Besides, we can notice the absence of any empty buffer occurrences when using the PS-DASH algorithm. 2) Energy Consumption Evaluation: In these measure- ments, we refer to a Multi-homed client that watch different
Fig. 7. Average Bitrate Enhancement of MP-DASH Algorithm Figure 7 shows the Cumulative Distributed Function (CDF) of the average enhancement calculated from these sets of “xperiments, when comparing our algorithm using the Parallel Streaming over DASH to the standard Dash (single transmis- sion) using the highest connection.The result shows quality enhancement between 10% to 95% in 80% from the scenarios, while 10% from the 192 scenarios were subject to quality degradation in comparison to single transmission. Besides, we can notice the absence of any empty buffer occurrences when using the PS-DASH algorithm. 2) Energy Consumption Evaluation: In these measure- ments, we refer to a Multi-homed client that watch different
video content,each video content has different bitrate repre- sentations. Each scenario is repeated two or three times using different speeds (as Show in Table I). The objective of these measurements is to check the network utilization in different scenarios, to ensure that our algorithm doesn’t use the parallel streaming when the maximum quality is reached through the speed of the highest single connection.
video content,each video content has different bitrate repre- sentations. Each scenario is repeated two or three times using different speeds (as Show in Table I). The objective of these measurements is to check the network utilization in different scenarios, to ensure that our algorithm doesn’t use the parallel streaming when the maximum quality is reached through the speed of the highest single connection.
Figure 8 represents the average video bitrate received for different video contents using different sets of network speeds. The Parallel Streaming usage is mentioned on top of each result. One can observe that the parallel streaming usage is decreasing in all cases when the highest available quality can be achieved through one network connection. For example, in the first video content, we have two available qualities, and we make two measurements that refer to two different sets of throughput. The result shows that parallel streaming usage is decreased from 70% to 18% while the highest video quality (1013 kbps) can be achieved through the second connection throughput (2Mbps) alone. In all scenarios, we observe the same conclusion with a different variation of decreasing. Fig. 8. Parallel Streaming usage in function on speed variations for different video contents
Figure 8 represents the average video bitrate received for different video contents using different sets of network speeds. The Parallel Streaming usage is mentioned on top of each result. One can observe that the parallel streaming usage is decreasing in all cases when the highest available quality can be achieved through one network connection. For example, in the first video content, we have two available qualities, and we make two measurements that refer to two different sets of throughput. The result shows that parallel streaming usage is decreased from 70% to 18% while the highest video quality (1013 kbps) can be achieved through the second connection throughput (2Mbps) alone. In all scenarios, we observe the same conclusion with a different variation of decreasing. Fig. 8. Parallel Streaming usage in function on speed variations for different video contents

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