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Stochastic QoE-aware optimization of multisource multimedia content delivery for mobile cloud

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Abstract

The increasing popularity of mobile video streaming in wireless networks has stimulated growing demands for efficient video streaming services. However, due to the time-varying throughput and user mobility, it is still difficult to provide high quality video services for mobile users. Our proposed optimization method considers key factors such as video quality, bitrate level, and quality variations to enhance quality of experience over wireless networks. The mobile network and device parameters are estimated in order to deliver the best quality video for the mobile user. We develop a rate adaptation algorithm using Lyapunov optimization for multi-source multimedia content delivery to minimize the video rate switches and provide higher video quality. The multi-source manager algorithm is developed to select the best stream based on the path quality for each path. The node joining and cluster head election mechanism update the node information. As the proposed approach selects the optimal path, it also achieves fairness and stability among clients. The quality of experience feature metrics like bitrate level, rebuffering events, and bitrate switch frequency are employed to assess video quality. We also employ objective video quality assessment methods like VQM, MS-SSIM, and SSIMplus for video quality measurement closer to human visual assessment. Numerical results show the effectiveness of the proposed method as compared to the existing state-of-the-art methods in providing quality of experience and bandwidth utilization.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, University of Engineering and Technology, Lahore, 54890, Pakistan

    Muhammad Saleem, Yasir Saleem & Muhammad Faisal Hayat

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  1. Muhammad Saleem
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Appendix

Appendix

Proof of Lemma 1:

For user i ∈ N, according to Eq. (15), we can write the following inequality.

$$Z_{i} \left( {t + 1} \right) = max\left[ {Z_{i} \left( t \right) - AB_{est} \left( t \right) , 0} \right] + C^{k} \left( t \right)$$
(25)
$$Z_{i} \left( {t + 1} \right) - Z_{i} \left( t \right) \ge C^{k} \left( t \right) - AB_{est} \left( t \right)$$
(26)

Take the sum of the inequality over t ∈ {0,….,T}, and write new Eq. (27),

$$\frac{{Z_{i} \left( T \right)}}{T} - \frac{{Z_{i} \left( 0 \right)}}{T} + \frac{1}{T}\mathop \sum \limits_{t = 0}^{T} AB_{est} \left( t \right) \ge \frac{1}{T}\mathop \sum \limits_{t = 0}^{T} C^{k} \left( t \right)$$
(27)
$$Z_{i} \left( 0 \right) = 0$$

We take the expectation and limit of t in Eq. (27). We have inequality in Eq. (28).

$$\mathop {\lim }\limits_{T \to \infty } \frac{{{\mathbb{E}}\left\{ {Z_{i} \left( T \right)} \right\}}}{T} + \mathop {\lim }\limits_{T \to \infty } \frac{1}{T}\mathop \sum \limits_{t = 0}^{T} AB_{est} \left( t \right) \ge \mathop {\lim }\limits_{T \to \infty } \frac{1}{T}\mathop \sum \limits_{t = 0}^{T} C^{k} \left( t \right)$$
(28)

It the queue Zi(T) is mean rate stable, the Eq. (29) will be:

$$\mathop {\lim }\limits_{T \to \infty } \frac{{\left\{ {Z_{i} \left( T \right)} \right\}}}{T} = 0$$
(29)

Considering the inequality in Eq. (28) the constraint

Cs(t) ≤ ABest is satisfied. Hence the Lemma 1 is proved.

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Saleem, M., Saleem, Y. & Hayat, M.F. Stochastic QoE-aware optimization of multisource multimedia content delivery for mobile cloud. Cluster Comput 23, 1381–1396 (2020). https://doi.org/10.1007/s10586-019-03007-y

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